Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, t...Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.展开更多
To solve single-objective constrained optimization problems,a new population-based evolutionary algorithm with elite strategy(PEAES) is proposed with the concept of single and multi-objective optimization.Constrained ...To solve single-objective constrained optimization problems,a new population-based evolutionary algorithm with elite strategy(PEAES) is proposed with the concept of single and multi-objective optimization.Constrained functions are combined to be an objective function.During the evolutionary process,the current optimal solution is found and treated as the reference point to divide the population into three sub-populations:one feasible and two infeasible ones.Different evolutionary operations of single or multi-objective optimization are respectively performed in each sub-population with elite strategy.Thirteen famous benchmark functions are selected to evaluate the performance of PEAES in comparison of other three optimization methods.The results show the proposed method is valid in efficiency,precision and probability for solving single-objective constrained optimization problems.展开更多
Background Aiming at free-view exploration of complicated scenes,this paper presents a method for interpolating views among multi RGB cameras.Methods In this study,we combine the idea of cost volume,which represent 3 ...Background Aiming at free-view exploration of complicated scenes,this paper presents a method for interpolating views among multi RGB cameras.Methods In this study,we combine the idea of cost volume,which represent 3 D information,and 2 D semantic segmentation of the scene,to accomplish view synthesis of complicated scenes.We use the idea of cost volume to estimate the depth and confidence map of the scene,and use a multi-layer representation and resolution of the data to optimize the view synthesis of the main object.Results/Conclusions By applying different treatment methods on different layers of the volume,we can handle complicated scenes containing multiple persons and plentiful occlusions.We also propose the view-interpolation→multi-view reconstruction→view interpolation pipeline to iteratively optimize the result.We test our method on varying data of multi-view scenes and generate decent results.展开更多
As a class of effective methods for incomplete multi-view clustering,graph-based algorithms have recently drawn wide attention.However,most of them could use further improvement regarding the following aspects.First,i...As a class of effective methods for incomplete multi-view clustering,graph-based algorithms have recently drawn wide attention.However,most of them could use further improvement regarding the following aspects.First,in some graph-based models,all views are forced to share a common similarity graph regardless of the severe consistency degeneration due to incomplete views.Next,similarity graph construction and cluster analysis are sometimes performed separately.Finally,the contribution difference of individual views is not always carefully considered.To address these issues simultaneously,this paper proposes an incomplete multi-view clustering algorithm based on auto-weighted fusion in partition space.In our algorithm,the information of cluster structure is introduced into the process of similarity learning to construct a desirable similarity graph,information fusion is performed in partition space to alleviate the negative impact brought about by consistency degradation,and all views are adaptively weighted to reflect their different contributions to clustering tasks.Finally,all the subtasks are collaboratively optimized in a united framework to reach an overall optimal result.Experimental results show that the proposed method compares favorably with the state-of-the-art methods.展开更多
基金supported in part by NUS startup grantthe National Natural Science Foundation of China (52076037)。
文摘Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.
文摘To solve single-objective constrained optimization problems,a new population-based evolutionary algorithm with elite strategy(PEAES) is proposed with the concept of single and multi-objective optimization.Constrained functions are combined to be an objective function.During the evolutionary process,the current optimal solution is found and treated as the reference point to divide the population into three sub-populations:one feasible and two infeasible ones.Different evolutionary operations of single or multi-objective optimization are respectively performed in each sub-population with elite strategy.Thirteen famous benchmark functions are selected to evaluate the performance of PEAES in comparison of other three optimization methods.The results show the proposed method is valid in efficiency,precision and probability for solving single-objective constrained optimization problems.
文摘Background Aiming at free-view exploration of complicated scenes,this paper presents a method for interpolating views among multi RGB cameras.Methods In this study,we combine the idea of cost volume,which represent 3 D information,and 2 D semantic segmentation of the scene,to accomplish view synthesis of complicated scenes.We use the idea of cost volume to estimate the depth and confidence map of the scene,and use a multi-layer representation and resolution of the data to optimize the view synthesis of the main object.Results/Conclusions By applying different treatment methods on different layers of the volume,we can handle complicated scenes containing multiple persons and plentiful occlusions.We also propose the view-interpolation→multi-view reconstruction→view interpolation pipeline to iteratively optimize the result.We test our method on varying data of multi-view scenes and generate decent results.
基金Acknowledgment This work was supported by the National Natural Science Foundation of China(No.61976247)the Basic Ability Promotion Project of Guangxi Middle-Aged and Young University Teacher。
文摘As a class of effective methods for incomplete multi-view clustering,graph-based algorithms have recently drawn wide attention.However,most of them could use further improvement regarding the following aspects.First,in some graph-based models,all views are forced to share a common similarity graph regardless of the severe consistency degeneration due to incomplete views.Next,similarity graph construction and cluster analysis are sometimes performed separately.Finally,the contribution difference of individual views is not always carefully considered.To address these issues simultaneously,this paper proposes an incomplete multi-view clustering algorithm based on auto-weighted fusion in partition space.In our algorithm,the information of cluster structure is introduced into the process of similarity learning to construct a desirable similarity graph,information fusion is performed in partition space to alleviate the negative impact brought about by consistency degradation,and all views are adaptively weighted to reflect their different contributions to clustering tasks.Finally,all the subtasks are collaboratively optimized in a united framework to reach an overall optimal result.Experimental results show that the proposed method compares favorably with the state-of-the-art methods.
