• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

    2022-08-24 12:56:18NorahSalehAlghamdiSamiBourouisandNizarBouguila
    Computers Materials&Continua 2022年7期

    Norah Saleh Alghamdi, Sami Bourouisand Nizar Bouguila

    1Department of Computer Sciences, College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia

    2College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

    3The Concordia Institute for Information Systems Engineering (CIISE), Concordia University,Montreal, QC H3G 1T7, Canada

    Abstract: Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications.In particular, recognizing human activities with accurate results have become a topic of high interest.Although the current tools have reached remarkable successes, it is still a challenging problem due to various uncontrolled environments and conditions.In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some realworld applications.In particular, two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed.These models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture components.The choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed distributions.In addition, deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice properties.Moreover, a learning step via variational Bayesian setting is presented in a flexible way.The priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed form.Experimental results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.

    Keywords: Infinite Gamma mixture model; variational Bayes; hierarchical Dirichlet process; Pitman-Yor process; texture classification; human action recognition

    1 Introduction and Literature Review

    Data clustering has been the subject of wide research to the present days [1-4].The goal of clustering is to group the observed data into separate subgroups, such as the data in each subgroup shows some similarities to each other.Various clustering approaches have been developed in the past and some of them are based on distance metrics (such as the K-means and SOM algorithms).It is noteworthy that these algorithms are sensitive to noise, are not flexible when dealing with incomplete data, and may not definitely capture heterogeneity inherent in complex datasets.Another important alternative is the model-based clustering approach [5].It is effective for modelling the structure of clusters since data are supposed to be produced by some distributions.In particular, finite mixture models (such as Gaussian mixture model) have shown to be effective (in terms of discovering complex patterns and grouping them into similar clusters) for many computer vision and pattern recognition applications [6].Nevertheless, Gaussian model cannot fit well complex non-Gaussian shapes.To cope with the disadvantages related to conventional Gaussian assumption, many contributions have occurred to develop other flexible mixture models and were interested to non-Gaussian behavior of real datasets [7,8].For instance,Gamma (GaMM) mixtures have demonstrated to offer high flexibility and ease of use than Gaussian [9] for many image processing and pattern recognition problems.This is due their compact analytical form which is able to cover long-tailed distributions and to approximate data with outliers.This mixture has been used with success for a range of interesting problems [7,10-12]especially when dealing with proportional data.An illustration of how mixture models can be applied for data modelling, classification and recognition is given in Fig.1.

    Figure 1: Block diagram of application of mixture model for data classification and recognition

    Unfortunately, the inadequacy of finite mixture model has been apparent when selecting the appropriate number of mixture components.In other word, model selection (i.e., model’s complexity)is one of the difficult problems within finite mixture models.The crucial problem of“how many groups in the dataset?”still remains of great interest for various data mining fields since determining inappropriate number of clusters may conduct to poor generalization capability.This problem can be solved by considering an infinite number of components via nonparametric Bayesian methods[13,14], principally within“Dirichlet process (DP)”[15].Indeed, a Dirichlet Process (DP) is a parameterized stochastic process characterized by a base distribution and it can be defined as a probability distribution over discrete distributions.Numerous studies have been devoted to infinite mixture models which are emerged to cope with the challenging problem of model selection.

    Two sound hierarchical Bayesian alternatives to the conventional DP named as hierarchical Dirichlet process (HDP) and hierarchical Pitman-Yor process (HPYP) have exposed encouraging results especially when dealing with modeling grouped data [16,17].Indeed,within hierarchical models,mixture components can be shared through data groups (i.e., parameters are shared among groups).In such cases, it is possible to make a Bayesian hierarchy on the DP and the base distribution of the DP is distributed according to another DP.

    Thus, the main contributions of this manuscript are first to extend our previous works about the Gamma mixture by investigating two efficient nonparametric hierarchical Bayesian models based on both Dirichlet and Pitman-Yor processes mixtures of Gamma distributions.Indeed, in order to reach enhanced modeling performance, we consider Gamma distribution which is able to cover longtailed distributions and to approximate accurately visual vectors.Another critical issue when dealing with mixture models is model parameters estimation.Accordingly, we propose to develop an effective variational Bayes learning algorithm to estimate the parameters of the implemented models.It is noteworthy to indicate that the complexity of variational inference-based algorithm still remains less than Markov Chain Monte Carlo-Based Bayesian inference and leads to faster convergence.Finally,the implemented hierarchical Bayesian models and the variational inference approaches are validated via challenging real-life problems namely human activity recognition and texture categorization.

    This manuscript is organized as follows.In Section 2 we introduce the hierarchical DP and PYP mixtures of Gamma distributions which are based on stick-breaking construction.In Section 3, we describe the details of our variational Bayes learning framework.Section 4 reports the obtained results, which are based on two challenging applications, to verify the merits and effectiveness of our framework, and Section 5 is devoted to conclude the manuscript.

    2 Model Specification

    In this section, we start by briefly presenting finite Gamma mixture model and then we present our nonparametric frameworks based on hierarchical Dirichlet and Pitman-Yor processes mixtures.

    2.1 Finite Gamma Mixture Model

    If aD-dimensional random vector= (Y1,...,YD) is distributed according to a multidimensional Gamma distribution, then its probability density function (pdf) is defined as:

    2.2 Hierarchical Dirichlet Process Mixture Model

    The hierarchical Dirichlet process (HDP) is an an effective nonparametric Bayesian method to modelling grouped data, which allows the mixture models to share components.Here, observed data are arranged into groups (i.e., mixture model) that we want to make them statistically linked.HDP It is built on the Dirichlet process (DP) as well described in [17] for each group data.It is noteworthy that the DP has acquired popularity in machine learning to handle nonparametric problems [18].The DP was presented as a prior on probability distributions and this makes it extremely appropriate for specifying infinite mixture models thanks to the use of the stick-breaking process [19].In the case of hierarchical Dirichlet process (HDP), the DPs for all groups share a base distribution which is itself distributed according to a Dirichlet process.Let’s assume that we have a grouped data set Y separated intoMgroups, such that each group is associated with a DPGj, thus the HDP takes part an indexed set of DPsGjthat share a global (or base) distributionG0which is itself distributed as a DP with base distributionHand concentration parameter γ:

    Here, the hierarchical Dirichlet process is represented using the stick-breaking construction[19,20].The global measureG0is distributed according toDP(γ,H) and it can be expressed as

    where δΩkrepresents an atom concentrated at Ωk, and {Ωk} is a set of independent random variables drawn fromH.The variable {ψk} are denoted as stick-breaking that verify= 1.AsG0is defined as the base distribution of the DPGjand has the stick-breaking representation as shown in Eq.(3),thenGjincludes all the atoms Ωkwith distinct weights (by following the property of Dirichlet process[18]).On the other side, we carry out another stick-breaking process to construct each group-level DPGjaccording to [21] as

    where {πjt} is a set of stick-breaking weights which shall be positive and sum to one and δωjtare grouplevel Dirac delta atoms at ωjt.As ωjt(group-level atom) is distributed according toG0, thus it will take onto Ωk(base-level atoms) with probability ψk.

    Next, we introduce a latent indicatorWjtkas an indicator variable, such thatWjtk∈{0,1} (in order to indicate which group-level atom maps to).Wjtk= 1 if ωjt(group-level atom) maps to the Ωk(globallevel atom) that is indexed byk; otherwise,Wjtk= 0.Accordingly, we can have ωjt=.By this way, there is no need to keep a representation for ωjt.The indicator variable= (Wjt1,Wjt2,...) is distributed as:

    Given that ψ is a function of ψ′according to Eq.(4), it is possible to rewrite the indicator variablep() as:

    According to Eq.(4) the stick lengths ψ′are drawn from a specific Beta distribution and their realization is determined as

    To complete the description of the HDP mixture model, given a grouped observation (data) Y,we associate each data pointwith a factor(hereiindexes the data in each groupj) such thatand= (θj1,θj2,...) are distributed according toF(θji) andGj, respectively.In this case the likelihood function can be written as:

    whereF(θji) is the probability distribution ofYjigiven θji.The base distributionHprovides the prior distribution for θji.This setting (i.e., hierarchical Dirichlet process (HDP) mixture model) plays an important role and ensures that each group is associated with a mixture model, and the components of the mixture are shared across different groups.

    As each θjiis distributed according toGj(see Eq.(9)), it takes the value ωjtwith probability πjt.We then introduce another indicator variableZjit∈{0,1} for θjias

    That is, the indicatorZjitis used to indicate which component θjibelongs to.In particular,Zjitis equal to 1 if θjiis associated with componentt(also maps to the group-level atom ωjt); else,Zjit= 0.Thus, we can write.As ωjtmaps to Ωk, we then can write.

    Based on the stick-breaking construction of the DP (see Eq.(5)), we notice thatis a function of, therefore the previous equation becomes

    Finally, according to Eq.(5), the prior distribution of π′is a Beta and it is given as follows:

    2.3 Hierarchical Pitman-Yor Process Mixture Model

    The Pitman-Yor process (PYP) [22] is a two-parameter extension to the DP (i.e., is a generalization of DP) that permits modelling heavier-tailed distributions.It can be applied to build hierarchical models.It offers a sophisticated way to cluster data such that the number of clusters is unknown.It is characterized by an additional discount parameter γain addition to the concentration parameter γb, that satisfying the 0<γa<1,γb>-γa.Similar to DP, the sample drawn from PYP also associated a probability measureH[23].Here, a hierarchical Pitman-Yor process (HPYP) is introduced where the base measure for a PYP is itself a draw from a PYP.Specifically, HPYP defines the global-level measureG0and group-level distributionGj(that is the indexedGjshares a same baseG0which itself follows a PYP).This behavior makes the HPYP especially suitable for complex visual data modeling and classification.We can use the HPYP to cluster data by applying the stick-breaking construction that defines the base measure as follows:

    where {Λk} is a set of independent random variables drawn fromHand δΛkis an atom (probability mass) at Λk.The random variables ηkrepresent the stick-breaking weights that satisfying= 1.The stick-breaking representation for the group-level PYPGjis expressed as:

    where {pjt} are the stick-breaking weights.ψjtis the atom of second-level PYP that is distributed according toG0.Then, a global-level indicator variablesIand a group-level indicator variablesCare introduced.Here,Cis used to map θjito group-level atom ψjtand the indicatorIis used to map the atom θjito base-level atom Λk.

    2.4 Hierarchical Infinite Gamma Mixture Model

    In this subsection, we introduce two hierarchical infinite mixture models with Gamma distributions.In this case, each vector= (Yji1,...,YjiD),fromthe grouped data, is drawn froma hierarchical infinite Gamma mixture model.Then, the two likelihood functions of these hierarchical models given the unknown parameters of Gamma and latent variables can be expressed as follows:

    Next, we have to place conjugate distributions over the unknown parameters α and β.As α and β are positive, then it is convenient that they follow Gamma distributions G(.).Thus, we have

    3 Model Learning via Variational Bayes

    Variational inference [3,24] is a well-defined method deterministic approximation method that is used in order to approximate posterior probability via an optimization process.In this section,we propose to develop a variational learning framework of our hierarchical infinite Gamma mixture models.Here,Θ=represents both unknown and the latent variables.Our objective is to estimate a suitable approximationq(Θ) for the true posterior distributionp(Θ|Y) via a process of maximizing the lower bound of lnp(Y) given as

    In particular, we adopt one of the most successfully variational inference techniques namely the factorial approximation (or mean fields approximation) [3], which is able to offer effective updates.Thus, we apply this method to fully factorizeq(Θ) of on HDPGaM and HPYPGaM mixtures into disjoint factors.Then, we apply a truncation method as previously applied in [20] to truncate the variational approximations into global truncation levelKand group truncation levelTas follow:

    where the truncation levelsKandTwill be optimized over the learning procedure.The approximated posterior distribution is then factorized as

    For a specific variational factorqs(Θs), the general equation of the optimal solution is expressed as [3]:

    where〈.〉i/=sdenotes an expectation with respect to all the distributions ofqi(Θi) except fori=s.The parametric forms for the variational posteriors (for each factor) are determined on the basis of Eq.(26) as

    where the corresponding hyperparameters in the above equations can be calculated as a similar way in[25,26].The complete variational Bayes inference algorithms of both HDPGaM and HPYPGaM are summarized in Algorithm 1 and Algorithm 2, respectively.

    Algorithm 1: HDPGaM: Proposed Hierarchical Dirichlet process gamma mixture algorithmimages/BZ_1121_265_950_1515_1565.png

    Algorithm 2: HPYPGaM: Proposed Hierarchical Pitman-Yor process gamma mixture algorithmimages/BZ_1121_265_1730_1515_2347.png

    4 Experimental Results

    The principal purpose of the experiment section is to investigate the performance of the developed two frameworks based on HDP mixture and HPYP mixture model with Gamma distributions.Hence,we propose to compare them with other statistical models using two challenging applications: Texture categorization and human action categorization.In all these experiments, the global truncation levelKand the group level truncation levelTare both initialized to 120 and 60, respectively.For HDP mixture, we set the hyperparameters of the stick lengths γ and λ as (0.25, 0.25).The parameters of HPYP mixture γa,γb,βaand βbare initialized to (0.5, 0.25, 0.5, 0.25).The hyperparameters of Gamma base distribution are initialized by sampling from priors.

    4.1 Texture Classification

    In thisworkwe are primarily motivated by the problem of modeling and classifying texture images.Contrary to natural images which include certain objects and structures, texture images are very special case of images that do not include a well-defined shape.Texture pattern is one of the most important elements in visual multimedia content analysis.It forms the basis for solving complex machine learning and computer vision tasks.In particular, texture classification supports a wide range of applications, including information retrieval, image categorization [27-30], image segmentation [27,31,32], material classification [33], facial expression recognition [29], and object detection [28,34].The goal here to classify texture images using the two hierarchical infinite mixtures and also by incorporating three different representations (to extract relevant features from images) from the literature, namely Local Binary Pattern (LBP) [35], Local Binary Pattern (LBP) [35], scale-invariant feature transform (SIFT) [36], and dense micro-block difference (DMD) [37].A deep review for these methods is outside the scope of current work.Instead, we focus on some powerful feature extraction methods that have shown interesting state-of-the-art results.

    4.1.1 Methodology

    For this application, we start by extracting features from input images and then we model them using the proposed HDPGaM and HPYPGaM.Each imageIjis considered as a group and is related to a infinite mixture modelGj.Next, every vectorYjiofIjis assumed generated fromGj, whereGjrepresents visual words.The next step is to generate a global vocabulary to share it among all groups viaG0(global infinite model).It is noteworthy that the the building of the visual vocabulary, here, is part of our hierarchical models and therefore, the size of the vocabulary (i.e., number of components)is is inferred automatically from the data thanks to the characteristic of nonparametric Bayesian models.Regarding SIFT features, the bag-of-visual-words model is adopted here to calculate the histogram of visual words from each input image.Regarding the set of DMD descriptors, these are obtained after extracting DMD features and then encoding them though th Fisher vector method as proposed in[37].The resulting descriptors are able to attain good discrimination thanks to their invariance with respect to scale, resolution, and orientation.Finally, each image is represented by a multidimensional vector of high-order statistics1The Matlab code for the features is available at http://www.cs.tut.fi/ mehta/texturedmd..

    4.1.2 Dataset and Results

    We conducted our experiments of texture classification using the proposed hierarchical HDP Gamma mixture (referred to as HDPGaM) and HPYP Gamma mixture (referred to as HPYPGaM)on three publicly available databases.The first one namely UIUCTex [38] contains 25 texture classes and each class has 40 images.The second dataset namely UMD [39] contains 25 textures classes containing each one 40 images.The third dataset namely KTH-TIPS [33] includes 10 classes and each one contains 81 images.Some sample texture images from each class and each dataset are shown in Fig.2.We use 10-fold cross-validation technique to partition these databases and to study the performance.In addition, the evaluation process and the obtained results are based on 30 runs.

    In order to quantify the performance of the proposed frameworks (HDPGaM and HPYPGaM),we proceed by evaluating and comparing the obtained results with seven other methods namely infinite mixture of Gaussian distribution (inGM), infinite mixture of generalized Gaussian distribution (inGGM), infinite mixture of multivariate generalized Gaussian distribution (inMGGM),Hierarchical Dirichlet Process mixture of Gaussian distribution (HDPGM), hierarchical Pitman-Yor process mixture of Gaussian distribution (HPYPGM), Hierarchical Dirichlet Process mixture of generalized Gaussian distribution (HDPGGM), and hierarchical Pitman-Yor process mixture of generalized Gaussian distribution (HPYPGGM).

    Figure 2: Texture samples in different categories for different datasets (a) KTH-TIPS, (b) UIUCTex,and (c) UMD dataset

    We run all methods 30 times and calculate the average classification accuracy which are depicted in Tabs.1-3 respectively.According to these results, we can notice that HDPGaM and HPYPGaM have the highest achieved accuracy for the three databases in terms of the texture classification accuracy rate.It is noted that when comparing these results by considering the Student’s t-test, the differences in performance are statistically significant between our frameworks and the rest of methods.In particular, results indicate the benefits of our proposed models in terms of texture modeling and classification capabilities which surpass those obtained by HDPGM, HDPGGM,HPYPGM, andHPYPGGM.By contrast, the worst performance is obtained within the infinite Gaussian mixture models.It should be noted that the proposed frameworks outperform the other methods and that the three adopted feature extraction methods (SIFT, LBP and DMD).Thus, these results confirm the merit of the proposed methods.Due to the effectiveness of DMD descriptor for describing and modelling observed texture images, we also find that DMDachieves better accuracy compared with both SIFT and LBP.It shows the merits of DMD which is able to consider all possible fine details images at different resolutions.We also note that with HPYP mixture we can reach better results compared to HDP mixture and this is for all tested distributions.This can be explained by the fact that HPYP mixture model has better generalization capability and better capacity to model heavier-tailed distribution (PYP prior can lead to better modeling ability).

    Table 3: The average accuracy results of texture classification using different algorithms for the 3 texture-datasets using DMD features

    Table 1: The average accuracy results of texture classification using different algorithms for the 3 texture-datasets using SIFT features

    Table 2: The average accuracy results of texture classification using different algorithms for the 3 texture-datasets using LBP features

    Table 3: Continued

    4.2 Human Actions Categorization

    Visual multimedia recognition has been a challenging research topic which could attract many applications such as actions recognition [40,41], image categorization [42,43], biomedical image recognition [44], and facial expressions [29,30].In this work, we are focusing on a particular problem that has received a lot of attention namely Human actions recognition (HAR) through sequence of videos.Indeed, the intention of recognizing activities is to identify and analyze various human actions.At present, HAR is one the hot computer vision topics not only in research but also in industries where automatic identification of any activity can be useful, for instance, for monitoring, healthcare,robotics, and security-based applications [45].Recognizing manually activities is very challenging and time consuming.This issue has been addressed and so various tools have been implemented such as in [40,45-47].However, precise recognition of actions is still required using advanced and efficient algorithms in order to deal with complex situations such as noise, occlusions, and lighting.

    We perform here the recognition of Human activities using the proposed frameworks HDPGaM and HPYPGaM.Our methodology is outlined as following: First, we extract and normalize SIFT3D descriptor [40] from observed images.These features are then quantized as visual words via bag-ofwords (BOW) model and K-means algorithm [48].Then, these features are quantized as visual words via K-means algorithm [48].Then, a probabilistic Latent Semantic Analysis (pLSA) [49] is adopted to construct ad-dimensional vector.In particular, each imageIjis considered as a“group”and is associated with an infinite mixture modelGj.Thus, we suppose that Each SIFT3D feature vector is drawn from the infinite mixture modelGjand“visual words”denote mixture components ofGj.

    On the other hand, a global vocabulary is generated and shared between all groups via the global-modelG0of the proposed hierarchical models.This setting agrees with the purpose behind the hierarchical process mixture model.It is also noted that the building of the visual vocabulary is part of the hierarchical process mixture models and this step is not carried out separately via the k-means algorithm as many other approaches do.It is also noted that the generation of the visual vocabulary is part of our hierarchical process mixture models and this process is not performed separately via kmeans as many other approaches did.In fact, it is due to the property of the nonparametric Bayesian model that the number of components in the global-level mixture model can be deduced from the data.We conducted our experiments of actions recognition using a publicly dataset known as KTH human action dataset [46].This database contains 2391 sequences of different actions grouped into 6 classes.It also represents four scenarios (outdoors (s1), outdoors with scale variation (s2), outdoors with different clothes (s3) and indoors (s4)).Some scenarios from this dataset are given in Fig.3.We randomly divided this dataset into 2 subsets to train the developed frameworks and to evaluate its robustness.

    Our purpose through this application is to show the advantages of investigating our proposed hierarchical models HDPGaM and HPYPGaM over other conventional hierarchical mixtures and other methods from the state of the art.Therefore, we focused first on evaluating the performance of HDPGaM and HPYPGaM over Hierarchical Dirichlet Process mixture of Gaussian distribution(HDPGM), hierarchical Pitman-Yor process mixture of Gaussian distribution (HPYPGM), Hierarchical Dirichlet Process mixture of generalized Gaussian distribution (HDPGGM), and hierarchical Pitman-Yor process mixture of generalized Gaussian distribution (HPYPGGM).It is noted that we learned all the implemented models using variational Bayes.The average recognition performances of our frameworks and models based on HDP mixture and HPYP mixture are depicted in Tab.4.

    Figure 3: Sample frames of the KTH dataset actions with different scenarios

    Table 4: Average recognition performance (%) obtained using our frameworks and other models based on HDP mixture and HPYP mixture for KTH database

    As we can see in this table, the proposed frameworks were able to offer the highest recognition rates (82.27% for HPYPGaM and 82.13% for HDPGaM) among all tested models.For different runs, we have p-values<0.05 and therefore, the differences in accuracy between our frameworks and other models are statistically significant according to Student’s t-test.Next, we compared our models against other mixture models (here finite Gaussian mixture (GMM) and finite generalized Gaussian mixture (GGMM) and methods from the literature.The obtained results are given in Tab.5.

    Table 5: Average recognition performance (%) obtained using our frameworks and other methods from the literature for KTH database

    Accordingly, we can observe that models again are able to provide higher discrimination rate than the other methods.Clearly, these results confirm the effectiveness of our frameworks for activities modeling and recognition compared to other conventional Dirichlet and Pitman-Yor processes based on Gaussian distribution.Another remark is that our model HPYPGaM outperforms our second model HDPGaM for this specific application and this demonstrates the advantages of using hierarchical Pitman-Yor process over Dirichlet process which is flexible enough to be used for such recognition problem.

    5 Conclusions

    In this paper two non-parametric Bayesian frameworks based on both hierarchical Dirichlet and Pitman-Yor processes and Gamma distribution are proposed.The Gamma distribution is considered because of its flexibility for semi-bounded data modelling.Both frameworks are learned using variational inference which has certain advantages such as easy assessment of convergence and easy optimization by offering a trade-off between frequentist techniques and MCMC-based ones.An important property of our approach is that it does not need the specification of the number of mixture components in advance.We carried out experiments on texture categorization and human action recognition to demonstrate the performance of our models which can be used further for a variety of other computer vision and pattern recognition applications.

    Acknowledgement:The authors would like to thank Taif University Researchers Supporting Project number(TURSP-2020/26),Taif University,Taif,Saudi Arabia.They would like also to thank Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R40), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

    Funding Statement:The authors would like to thank Taif University Researchers Supporting Project number(TURSP-2020/26),Taif University,Taif,Saudi Arabia.They would like also to thank Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R40),Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    国产精品av久久久久免费| 国产精品久久久久成人av| 中文字幕av电影在线播放| 最近手机中文字幕大全| 啦啦啦视频在线资源免费观看| 久久午夜福利片| 午夜激情av网站| 欧美 日韩 精品 国产| 老女人水多毛片| 国产精品久久久av美女十八| 丁香六月天网| 国产精品久久久av美女十八| 欧美亚洲日本最大视频资源| 国产日韩欧美视频二区| 如何舔出高潮| av.在线天堂| 久久毛片免费看一区二区三区| 成年动漫av网址| 国产亚洲精品第一综合不卡| 天天躁狠狠躁夜夜躁狠狠躁| 飞空精品影院首页| 女的被弄到高潮叫床怎么办| 十八禁高潮呻吟视频| 一个人免费看片子| 考比视频在线观看| 免费大片黄手机在线观看| 亚洲欧美成人精品一区二区| 亚洲av综合色区一区| 欧美精品av麻豆av| 欧美精品av麻豆av| 亚洲国产精品一区三区| av网站在线播放免费| 亚洲熟女精品中文字幕| 久久久久久久久久久久大奶| 这个男人来自地球电影免费观看 | 99精国产麻豆久久婷婷| 国产黄色免费在线视频| 青草久久国产| 我的亚洲天堂| 久久久久久人妻| 国产一区二区三区综合在线观看| 久久99精品国语久久久| 看免费av毛片| 久久精品国产亚洲av天美| 美女高潮到喷水免费观看| 午夜av观看不卡| 9色porny在线观看| 如何舔出高潮| 99国产综合亚洲精品| 亚洲欧洲国产日韩| 最近手机中文字幕大全| 秋霞伦理黄片| 亚洲av国产av综合av卡| 国产成人av激情在线播放| 黄网站色视频无遮挡免费观看| 亚洲人成网站在线观看播放| 精品国产一区二区三区久久久樱花| 下体分泌物呈黄色| 国产成人精品福利久久| 丝瓜视频免费看黄片| 曰老女人黄片| 久久精品熟女亚洲av麻豆精品| 青草久久国产| 国产精品av久久久久免费| 老汉色av国产亚洲站长工具| 久久久久久久亚洲中文字幕| 午夜激情av网站| av国产久精品久网站免费入址| 欧美人与性动交α欧美软件| 秋霞在线观看毛片| av线在线观看网站| 免费看不卡的av| 久久av网站| 国产无遮挡羞羞视频在线观看| 久久国产亚洲av麻豆专区| 日韩免费高清中文字幕av| 亚洲av免费高清在线观看| 免费观看无遮挡的男女| www.精华液| a 毛片基地| 午夜免费观看性视频| 精品人妻偷拍中文字幕| 一级毛片电影观看| 18禁动态无遮挡网站| 九草在线视频观看| 亚洲精品乱久久久久久| 国产乱来视频区| 亚洲第一青青草原| 亚洲伊人久久精品综合| 久久久久精品性色| 欧美黄色片欧美黄色片| 国产精品秋霞免费鲁丝片| 好男人视频免费观看在线| av线在线观看网站| 久久久久久久久久久久大奶| av国产精品久久久久影院| 国产欧美日韩一区二区三区在线| 欧美激情高清一区二区三区 | 18禁裸乳无遮挡动漫免费视频| 日日撸夜夜添| 五月伊人婷婷丁香| 视频区图区小说| 亚洲国产av新网站| 天堂8中文在线网| 久久久久精品性色| 一区二区三区激情视频| 国产爽快片一区二区三区| 久久国产精品男人的天堂亚洲| 伦理电影大哥的女人| av.在线天堂| 久久久久久久久久久久大奶| 在线天堂最新版资源| 亚洲激情五月婷婷啪啪| 亚洲天堂av无毛| 亚洲国产av新网站| 人人澡人人妻人| 2021少妇久久久久久久久久久| 久久精品久久久久久久性| 久久久久久久亚洲中文字幕| 尾随美女入室| 日本黄色日本黄色录像| 亚洲欧美精品自产自拍| 亚洲人成网站在线观看播放| 纯流量卡能插随身wifi吗| 亚洲国产成人一精品久久久| 日本欧美视频一区| 欧美激情极品国产一区二区三区| 男女下面插进去视频免费观看| 久久久精品国产亚洲av高清涩受| 黑人欧美特级aaaaaa片| 亚洲精品日本国产第一区| 日韩电影二区| 人体艺术视频欧美日本| 欧美日韩精品网址| 亚洲美女视频黄频| 热re99久久国产66热| 午夜久久久在线观看| 我要看黄色一级片免费的| 狂野欧美激情性bbbbbb| 亚洲欧洲日产国产| av一本久久久久| 国产黄色视频一区二区在线观看| 久久狼人影院| 国产成人精品久久久久久| 最黄视频免费看| 9色porny在线观看| 亚洲五月色婷婷综合| 巨乳人妻的诱惑在线观看| 国产男女超爽视频在线观看| 一区在线观看完整版| av一本久久久久| 人妻少妇偷人精品九色| 国产乱人偷精品视频| 欧美日本中文国产一区发布| 亚洲av国产av综合av卡| www.精华液| 女性被躁到高潮视频| 亚洲欧美色中文字幕在线| 亚洲成色77777| av免费观看日本| 搡老乐熟女国产| 男人爽女人下面视频在线观看| 精品人妻一区二区三区麻豆| 成人亚洲精品一区在线观看| 男人操女人黄网站| 久久影院123| 精品少妇一区二区三区视频日本电影 | 亚洲激情五月婷婷啪啪| 午夜久久久在线观看| 亚洲一区中文字幕在线| av在线老鸭窝| 亚洲成人av在线免费| 日本wwww免费看| 热re99久久国产66热| 999久久久国产精品视频| 中文字幕人妻丝袜一区二区 | 精品一区二区三区四区五区乱码 | 女性生殖器流出的白浆| 看非洲黑人一级黄片| 日本爱情动作片www.在线观看| 韩国av在线不卡| 尾随美女入室| 欧美日韩成人在线一区二区| 国产精品 欧美亚洲| 97精品久久久久久久久久精品| 亚洲三级黄色毛片| av.在线天堂| 波野结衣二区三区在线| 亚洲三区欧美一区| 在线亚洲精品国产二区图片欧美| 亚洲视频免费观看视频| 欧美激情极品国产一区二区三区| 欧美激情高清一区二区三区 | 久久久久国产网址| 丝袜美腿诱惑在线| 最黄视频免费看| 亚洲精品国产色婷婷电影| a级毛片黄视频| 精品人妻在线不人妻| 1024视频免费在线观看| 国产片内射在线| 成人18禁高潮啪啪吃奶动态图| 久久久久网色| 国产欧美日韩一区二区三区在线| a级片在线免费高清观看视频| 丝袜美腿诱惑在线| 99热网站在线观看| 精品国产国语对白av| 自线自在国产av| 女性生殖器流出的白浆| 日韩中字成人| 伦理电影免费视频| 日韩欧美精品免费久久| 蜜桃国产av成人99| 人人妻人人爽人人添夜夜欢视频| 色网站视频免费| 人人妻人人澡人人看| 国产精品亚洲av一区麻豆 | 国精品久久久久久国模美| 精品国产超薄肉色丝袜足j| 国产不卡av网站在线观看| 亚洲一区二区三区欧美精品| 亚洲av免费高清在线观看| 老汉色av国产亚洲站长工具| 国产人伦9x9x在线观看 | 成人国语在线视频| 在线观看国产h片| 久久 成人 亚洲| www.自偷自拍.com| 日本-黄色视频高清免费观看| 国产麻豆69| 久久午夜综合久久蜜桃| 丝瓜视频免费看黄片| 欧美xxⅹ黑人| 亚洲情色 制服丝袜| 久久国内精品自在自线图片| 91成人精品电影| 日本黄色日本黄色录像| 久久99精品国语久久久| 伦理电影免费视频| 黄网站色视频无遮挡免费观看| 尾随美女入室| 午夜av观看不卡| 中文字幕av电影在线播放| 精品亚洲乱码少妇综合久久| 欧美日本中文国产一区发布| 少妇的丰满在线观看| 女的被弄到高潮叫床怎么办| 久久精品国产亚洲av高清一级| 国产精品 欧美亚洲| 日韩免费高清中文字幕av| 国产亚洲av片在线观看秒播厂| 国产精品国产av在线观看| 亚洲成人av在线免费| 大香蕉久久成人网| 亚洲av电影在线观看一区二区三区| 国产精品免费视频内射| 蜜桃在线观看..| 中文欧美无线码| 啦啦啦中文免费视频观看日本| 18禁观看日本| 国产熟女午夜一区二区三区| 激情视频va一区二区三区| 国产成人精品久久二区二区91 | 99久久综合免费| 午夜福利乱码中文字幕| 纵有疾风起免费观看全集完整版| 国产 一区精品| 成人国语在线视频| videossex国产| 丝袜在线中文字幕| 日韩欧美一区视频在线观看| 成年美女黄网站色视频大全免费| 免费久久久久久久精品成人欧美视频| 久久久久国产精品人妻一区二区| 少妇被粗大的猛进出69影院| 一区在线观看完整版| 免费观看无遮挡的男女| 久久av网站| 又粗又硬又长又爽又黄的视频| www.自偷自拍.com| 最新中文字幕久久久久| 美女视频免费永久观看网站| 免费观看在线日韩| 亚洲av男天堂| 黄片播放在线免费| 99热全是精品| 国产精品香港三级国产av潘金莲 | 777米奇影视久久| av不卡在线播放| 精品亚洲成a人片在线观看| 中文字幕另类日韩欧美亚洲嫩草| 日本wwww免费看| 春色校园在线视频观看| 久久精品国产亚洲av天美| 黄频高清免费视频| av国产久精品久网站免费入址| 亚洲精品美女久久av网站| 日韩三级伦理在线观看| 欧美人与性动交α欧美软件| 日韩制服丝袜自拍偷拍| 18+在线观看网站| 另类亚洲欧美激情| 纵有疾风起免费观看全集完整版| 亚洲欧洲国产日韩| 国产一区二区 视频在线| 99久国产av精品国产电影| 亚洲少妇的诱惑av| 看十八女毛片水多多多| 免费播放大片免费观看视频在线观看| 天堂8中文在线网| 欧美 日韩 精品 国产| 汤姆久久久久久久影院中文字幕| 少妇熟女欧美另类| av网站免费在线观看视频| 好男人视频免费观看在线| 久久久久国产网址| 中文字幕最新亚洲高清| 精品亚洲乱码少妇综合久久| 国产精品.久久久| 日本午夜av视频| av在线播放精品| 在线观看www视频免费| 亚洲国产看品久久| 老鸭窝网址在线观看| 亚洲精品国产色婷婷电影| 国产日韩欧美视频二区| 欧美激情高清一区二区三区 | 成人黄色视频免费在线看| 亚洲精品av麻豆狂野| 免费看av在线观看网站| 亚洲欧美成人精品一区二区| 亚洲美女黄色视频免费看| 久久ye,这里只有精品| 99九九在线精品视频| 男女午夜视频在线观看| 男女免费视频国产| 又黄又粗又硬又大视频| 欧美xxⅹ黑人| 美女国产高潮福利片在线看| 免费高清在线观看日韩| 亚洲综合色网址| 春色校园在线视频观看| 久久久久久久国产电影| 99国产精品免费福利视频| 制服诱惑二区| 看免费av毛片| 伊人久久大香线蕉亚洲五| 欧美日本中文国产一区发布| 天堂8中文在线网| 91成人精品电影| 亚洲人成77777在线视频| 99热网站在线观看| 国产精品嫩草影院av在线观看| 女的被弄到高潮叫床怎么办| 免费高清在线观看日韩| av又黄又爽大尺度在线免费看| 日韩中文字幕欧美一区二区 | 国产精品二区激情视频| 国产视频首页在线观看| 午夜免费男女啪啪视频观看| 夫妻午夜视频| a级片在线免费高清观看视频| 国产亚洲最大av| 久久这里有精品视频免费| 国产精品免费大片| 18+在线观看网站| 精品一区在线观看国产| 午夜福利,免费看| 大片电影免费在线观看免费| 亚洲伊人色综图| 美女高潮到喷水免费观看| 欧美日韩一级在线毛片| 又黄又粗又硬又大视频| 欧美av亚洲av综合av国产av | 国产精品一区二区在线观看99| 日韩中文字幕视频在线看片| 亚洲国产看品久久| 91久久精品国产一区二区三区| 久久久久网色| 18+在线观看网站| 尾随美女入室| 亚洲激情五月婷婷啪啪| 亚洲情色 制服丝袜| 国产成人欧美| 亚洲av日韩在线播放| 亚洲 欧美一区二区三区| 看免费成人av毛片| 久久午夜福利片| 美女脱内裤让男人舔精品视频| 日日摸夜夜添夜夜爱| 国产成人午夜福利电影在线观看| 国产精品麻豆人妻色哟哟久久| 欧美老熟妇乱子伦牲交| 日韩伦理黄色片| 国产免费视频播放在线视频| 欧美成人午夜精品| 免费在线观看完整版高清| 欧美人与性动交α欧美精品济南到 | 99九九在线精品视频| 久久久久久人妻| 在线看a的网站| 丰满乱子伦码专区| 热re99久久精品国产66热6| 亚洲经典国产精华液单| 国产精品三级大全| 精品亚洲乱码少妇综合久久| 男女啪啪激烈高潮av片| 国产日韩欧美视频二区| 观看av在线不卡| 午夜精品国产一区二区电影| 26uuu在线亚洲综合色| 精品99又大又爽又粗少妇毛片| 免费观看av网站的网址| 综合色丁香网| 乱人伦中国视频| 大陆偷拍与自拍| 丰满乱子伦码专区| 一边摸一边做爽爽视频免费| 大话2 男鬼变身卡| 国产精品亚洲av一区麻豆 | 一级毛片我不卡| 日韩精品免费视频一区二区三区| 国产一区亚洲一区在线观看| 久久精品aⅴ一区二区三区四区 | 日韩一区二区视频免费看| 精品一区在线观看国产| 日本黄色日本黄色录像| 天天躁日日躁夜夜躁夜夜| 亚洲欧美一区二区三区黑人 | 观看av在线不卡| 999精品在线视频| 免费人妻精品一区二区三区视频| 咕卡用的链子| 日韩视频在线欧美| 视频区图区小说| 欧美国产精品va在线观看不卡| 久久狼人影院| 26uuu在线亚洲综合色| 少妇猛男粗大的猛烈进出视频| 精品视频人人做人人爽| 国产成人91sexporn| 韩国av在线不卡| 日本色播在线视频| 精品亚洲成国产av| 男女高潮啪啪啪动态图| 亚洲精品美女久久久久99蜜臀 | 亚洲在久久综合| 久久久久视频综合| 一边摸一边做爽爽视频免费| 人体艺术视频欧美日本| 欧美少妇被猛烈插入视频| 亚洲国产成人一精品久久久| 母亲3免费完整高清在线观看 | 亚洲少妇的诱惑av| 亚洲av福利一区| 午夜福利一区二区在线看| 成人18禁高潮啪啪吃奶动态图| 黄色配什么色好看| 国产在视频线精品| 91午夜精品亚洲一区二区三区| 久久久精品区二区三区| 国产一区二区在线观看av| 飞空精品影院首页| 欧美亚洲 丝袜 人妻 在线| 午夜老司机福利剧场| 尾随美女入室| 一级毛片 在线播放| 热re99久久精品国产66热6| 一边摸一边做爽爽视频免费| 人成视频在线观看免费观看| 国产精品国产三级国产专区5o| 成人黄色视频免费在线看| 高清在线视频一区二区三区| 多毛熟女@视频| 国产精品久久久久成人av| a级毛片在线看网站| 国产成人欧美| 99久久中文字幕三级久久日本| 欧美97在线视频| 9色porny在线观看| 国产av精品麻豆| av女优亚洲男人天堂| 电影成人av| 97人妻天天添夜夜摸| 日韩熟女老妇一区二区性免费视频| 精品一区在线观看国产| 精品视频人人做人人爽| 国产一区亚洲一区在线观看| 国产精品麻豆人妻色哟哟久久| 久久久精品国产亚洲av高清涩受| 天堂俺去俺来也www色官网| 边亲边吃奶的免费视频| www日本在线高清视频| 国产精品av久久久久免费| av国产久精品久网站免费入址| 在线看a的网站| a 毛片基地| a级毛片在线看网站| 一级a爱视频在线免费观看| 成人亚洲欧美一区二区av| 久久人人爽av亚洲精品天堂| 国产色婷婷99| av片东京热男人的天堂| 久久久久久久大尺度免费视频| 欧美精品国产亚洲| 久久精品久久久久久久性| 亚洲精品,欧美精品| 在线免费观看不下载黄p国产| 欧美xxⅹ黑人| 嫩草影院入口| av有码第一页| 亚洲精品国产色婷婷电影| 午夜福利乱码中文字幕| 日韩av免费高清视频| 欧美精品高潮呻吟av久久| 777米奇影视久久| 美女午夜性视频免费| 日韩av免费高清视频| 一区二区日韩欧美中文字幕| 成人国产av品久久久| 青草久久国产| 亚洲成人一二三区av| 男人操女人黄网站| 飞空精品影院首页| 国产极品粉嫩免费观看在线| 极品少妇高潮喷水抽搐| 免费不卡的大黄色大毛片视频在线观看| 国产色婷婷99| 国产精品 欧美亚洲| 黄片小视频在线播放| 校园人妻丝袜中文字幕| 看十八女毛片水多多多| 9191精品国产免费久久| 90打野战视频偷拍视频| 春色校园在线视频观看| 你懂的网址亚洲精品在线观看| 美国免费a级毛片| 国产精品av久久久久免费| 男人爽女人下面视频在线观看| 美女大奶头黄色视频| 97人妻天天添夜夜摸| 男女下面插进去视频免费观看| 亚洲av免费高清在线观看| 精品国产一区二区三区久久久樱花| 91精品伊人久久大香线蕉| 久久久国产精品麻豆| 王馨瑶露胸无遮挡在线观看| 久久女婷五月综合色啪小说| 性色avwww在线观看| 免费观看在线日韩| 黄色视频在线播放观看不卡| 男女啪啪激烈高潮av片| 久久久精品区二区三区| 看非洲黑人一级黄片| 97在线人人人人妻| 亚洲国产欧美在线一区| 日韩精品免费视频一区二区三区| 久久ye,这里只有精品| 亚洲久久久国产精品| 91精品伊人久久大香线蕉| 欧美激情高清一区二区三区 | 国产精品国产三级国产专区5o| 日本色播在线视频| 伊人久久国产一区二区| 777米奇影视久久| 18+在线观看网站| 久久精品久久精品一区二区三区| 看十八女毛片水多多多| 亚洲欧美清纯卡通| 精品人妻偷拍中文字幕| 久久午夜综合久久蜜桃| 免费黄网站久久成人精品| 日韩一卡2卡3卡4卡2021年| 侵犯人妻中文字幕一二三四区| 亚洲av日韩在线播放| 最近的中文字幕免费完整| 精品人妻偷拍中文字幕| 少妇熟女欧美另类| 成人黄色视频免费在线看| 自线自在国产av| 日韩中文字幕视频在线看片| 秋霞伦理黄片| 午夜免费鲁丝| 国产高清不卡午夜福利| av不卡在线播放| 日韩人妻精品一区2区三区| 国产精品一二三区在线看| 国产欧美日韩综合在线一区二区| 日本91视频免费播放| 欧美日韩亚洲国产一区二区在线观看 | 另类精品久久| 亚洲伊人久久精品综合| 水蜜桃什么品种好| 国产成人精品久久二区二区91 | 卡戴珊不雅视频在线播放| 熟女电影av网| 亚洲五月色婷婷综合| 亚洲国产av新网站| 久久这里有精品视频免费| 在线观看美女被高潮喷水网站| 久久精品人人爽人人爽视色| 97精品久久久久久久久久精品| 一本色道久久久久久精品综合| www.av在线官网国产| 九色亚洲精品在线播放| 亚洲一级一片aⅴ在线观看| www.av在线官网国产| 少妇熟女欧美另类| 欧美精品国产亚洲| 久久久久久久久久人人人人人人| 国产亚洲一区二区精品| 国产激情久久老熟女| 熟女电影av网| 精品99又大又爽又粗少妇毛片|