A hybrid graph model for personalized recom-mendation,which is based on small world network and Bayesian network,is presented.The hybrid graph model has two-layers.The bottom level means user's layer and the upper...A hybrid graph model for personalized recom-mendation,which is based on small world network and Bayesian network,is presented.The hybrid graph model has two-layers.The bottom level means user's layer and the upper one means merchandise's layer.The user's layer is an undirected arcs graph,which describes the relation of the user's nodes by small world network.The undirected arcs inside the connected nodes of user's layer mean the similarity of the preference of users.These arcs are weighted by relational strength.The weight represents node's similarity or link's strength and intensity.Nodes in the same group are more similar to each other or more strongly connected.Users in a same group have the same or similar trendy of preferences.The merchandise's layer describes the relation of goods or produce to others.It is connected by directed links,which means an implicated definition among merchandises,a user that purchase certain merchandise also tends to purchase another.The properties and content of merchandise can be used to show the similarity of the merchandise.The relations between user's layer and merchandise's layer are connected by directed links.The start node of the directed links is a user node in user's layer belonging to some node group,which is gained by small world network.The end node of links is the node of some merchandise of the merchandise's layer.The directed links between the user's layer and the merchandise's layer are connected based on trade information of users.The strength of the relation between users and merchandises can be denoted by the probability parameter.The probability parameter shows a possibility of some users selecting for some merchandises. Firstly,algorithms for users clustering and for anal-ysis of new user interest are presented to construct a hybrid graph model.Two important characteristic parameters,which are in small-world network,are introduced. These are characteristic path length and clustering coefficient.New user interest analysis is to judge which clustering group is the best match by calculating the distance of the new user node to the others user nodes. Secondly,Bayesian network for causality of merchandises and users is constructed.It can be divided two parts,structure learning and parameter learning.The paper adopts the maximal mutual information principle to restrict complexity based on degree of Bayesian network.A new maximal mutual information entropy score function with restriction is defined and a maximum likelihood estimate algorithm is used to calculated parameter. Thirdly,recommending algorithm for new user is presented.In the algorithm,the initialized inputs can utilize some users information including the attributes and browsing process of a user.A proper user-clustering group will be gained by clustering matching with other users in small world network based on this information.Then all the other users nodes,which connect to this user,are selected based on a threshold of path length in the clustering.The recommended merchandise set of these users will be obtained by Bayesian network inference using these nodes as proofs.Finally,a set of recommendation of merchandise is presented for user according to their order of probability distribution. The paper uses the mean absolute error to evaluate the model and MovieLens database is selected.The experimentation shows that the model be accomplished to represent the relationships from user to user, merchandise to merchandise,and user to merchandise.The result shows that the hybrid graph model has a good performance in personalized recommendation.展开更多
A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec-...A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.展开更多
A finite-element model of the thermosetting epoxy asphalt mixture（EAM） microstructure is developed to simulate the indirect tension test（IDT）.Image techniques are used to capture the EAM microstructure which is di...A finite-element model of the thermosetting epoxy asphalt mixture（EAM） microstructure is developed to simulate the indirect tension test（IDT）.Image techniques are used to capture the EAM microstructure which is divided into two phases：aggregates and mastic.A viscoelastic constitutive relationship,which is obtained from the results of a creep test,is used to represent the mastic phase at intermittent temperatures.Model simulation results of the stiffness modulus in IDT compare favorably with experimental data.Different loading directions and velocities are employed in order to account for their influence on the modulus and the localized stress of the microstructure model.It is pointed out that the modulus is not consistent when the loading direction changes since the heterogeneous distribution of the mixture internal structure,and the loading velocity affects the localized stress as a result of the viscoelasticity of the mastic.The study results can provide a theoretical basis for the finite-element method,which can be extended to the numerical simulations of asphalt mixture micromechanical behavior.展开更多
Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk...Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk model(abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization(RPEM) algorithm is employed to estimate the posterior probabilities of each annotation keyword.Subsequently,a random walk process over the constructed label similarity graph is implemented to further mine the potential correlations of the candidate annotations so as to capture the refining results,which plays a crucial role in semantic based image retrieval.The contributions exhibited in this work are multifold.First,GMM is exploited to capture the initial semantic annotations,especially the RPEM algorithm is utilized to train the model that can determine the number of components in GMM automatically.Second,a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels,which is able to avoid the phenomena of polysemy and synonym efficiently during the image annotation process.Third,the random walk is implemented over the constructed label graph to further refine the candidate set of annotations generated by GMM.Conducted experiments on the standard Corel5 k demonstrate that GMM-RW is significantly more effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.展开更多
To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing m...To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.展开更多
In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial informat...In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.展开更多
A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region accor...A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.70671007China Postdoctoral Science Foundation under Grant No.20060390391
文摘A hybrid graph model for personalized recom-mendation,which is based on small world network and Bayesian network,is presented.The hybrid graph model has two-layers.The bottom level means user's layer and the upper one means merchandise's layer.The user's layer is an undirected arcs graph,which describes the relation of the user's nodes by small world network.The undirected arcs inside the connected nodes of user's layer mean the similarity of the preference of users.These arcs are weighted by relational strength.The weight represents node's similarity or link's strength and intensity.Nodes in the same group are more similar to each other or more strongly connected.Users in a same group have the same or similar trendy of preferences.The merchandise's layer describes the relation of goods or produce to others.It is connected by directed links,which means an implicated definition among merchandises,a user that purchase certain merchandise also tends to purchase another.The properties and content of merchandise can be used to show the similarity of the merchandise.The relations between user's layer and merchandise's layer are connected by directed links.The start node of the directed links is a user node in user's layer belonging to some node group,which is gained by small world network.The end node of links is the node of some merchandise of the merchandise's layer.The directed links between the user's layer and the merchandise's layer are connected based on trade information of users.The strength of the relation between users and merchandises can be denoted by the probability parameter.The probability parameter shows a possibility of some users selecting for some merchandises. Firstly,algorithms for users clustering and for anal-ysis of new user interest are presented to construct a hybrid graph model.Two important characteristic parameters,which are in small-world network,are introduced. These are characteristic path length and clustering coefficient.New user interest analysis is to judge which clustering group is the best match by calculating the distance of the new user node to the others user nodes. Secondly,Bayesian network for causality of merchandises and users is constructed.It can be divided two parts,structure learning and parameter learning.The paper adopts the maximal mutual information principle to restrict complexity based on degree of Bayesian network.A new maximal mutual information entropy score function with restriction is defined and a maximum likelihood estimate algorithm is used to calculated parameter. Thirdly,recommending algorithm for new user is presented.In the algorithm,the initialized inputs can utilize some users information including the attributes and browsing process of a user.A proper user-clustering group will be gained by clustering matching with other users in small world network based on this information.Then all the other users nodes,which connect to this user,are selected based on a threshold of path length in the clustering.The recommended merchandise set of these users will be obtained by Bayesian network inference using these nodes as proofs.Finally,a set of recommendation of merchandise is presented for user according to their order of probability distribution. The paper uses the mean absolute error to evaluate the model and MovieLens database is selected.The experimentation shows that the model be accomplished to represent the relationships from user to user, merchandise to merchandise,and user to merchandise.The result shows that the hybrid graph model has a good performance in personalized recommendation.
基金Supported by the National Natural Science Foundation of China(60505004,60773061)~~
文摘A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.
基金Program for New Century Excellent Talents in University(No. NCET-08-0118)Specialized Research Fund for the Doctoral Program of Higher Education (No. 20090092110049)
文摘A finite-element model of the thermosetting epoxy asphalt mixture（EAM） microstructure is developed to simulate the indirect tension test（IDT）.Image techniques are used to capture the EAM microstructure which is divided into two phases：aggregates and mastic.A viscoelastic constitutive relationship,which is obtained from the results of a creep test,is used to represent the mastic phase at intermittent temperatures.Model simulation results of the stiffness modulus in IDT compare favorably with experimental data.Different loading directions and velocities are employed in order to account for their influence on the modulus and the localized stress of the microstructure model.It is pointed out that the modulus is not consistent when the loading direction changes since the heterogeneous distribution of the mixture internal structure,and the loading velocity affects the localized stress as a result of the viscoelasticity of the mastic.The study results can provide a theoretical basis for the finite-element method,which can be extended to the numerical simulations of asphalt mixture micromechanical behavior.
基金Supported by the National Basic Research Program of China(No.2013CB329502)the National Natural Science Foundation of China(No.61202212)+1 种基金the Special Research Project of the Educational Department of Shaanxi Province of China(No.15JK1038)the Key Research Project of Baoji University of Arts and Sciences(No.ZK16047)
文摘Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk model(abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization(RPEM) algorithm is employed to estimate the posterior probabilities of each annotation keyword.Subsequently,a random walk process over the constructed label similarity graph is implemented to further mine the potential correlations of the candidate annotations so as to capture the refining results,which plays a crucial role in semantic based image retrieval.The contributions exhibited in this work are multifold.First,GMM is exploited to capture the initial semantic annotations,especially the RPEM algorithm is utilized to train the model that can determine the number of components in GMM automatically.Second,a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels,which is able to avoid the phenomena of polysemy and synonym efficiently during the image annotation process.Third,the random walk is implemented over the constructed label graph to further refine the candidate set of annotations generated by GMM.Conducted experiments on the standard Corel5 k demonstrate that GMM-RW is significantly more effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.
基金National Natural Science Foundation of China(Nos.61841303,61963023)Project of Humanities and Social Sciences of Ministry of Education in China(No.19YJC760012)。
文摘To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.
基金supported by the National Natural Science Foundation of China (Grant No.61275010,61077079)the State Key Program of National Natural Science Foundation of Heilongjiang Province of China (No.ZD201216)the Fundamental Research Funds for the Central Universities (No.HEUCF130820)
文摘In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.
文摘A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.
