The Experts below are selected from a list of 5097 Experts worldwide ranked by ideXlab platform
Dimitris N Metaxas - One of the best experts on this subject based on the ideXlab platform.
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unsupervised image categorization by hypergraph partition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Co-Authors: Yuchi Huang, Fengjun Lv, Yihong Gong, Dimitris N MetaxasAbstract:We present a framework for unsupervised image categorization in which images containing specific objects are taken as vertices in a hypergraph and the task of image clustering is formulated as the problem of hypergraph partition. First, a novel method is proposed to select the region of interest (ROI) of each image, and then Hyperedges are constructed based on shape and appearance features extracted from the ROIs. Each vertex (image) and its k-nearest neighbors (based on shape or appearance descriptors) form two kinds of Hyperedges. The weight of a hyperedge is computed as the sum of the pairwise affinities within the hyperedge. Through all of the Hyperedges, not only the local grouping relationships among the images are described, but also the merits of the shape and appearance characteristics are integrated together to enhance the clustering performance. Finally, a generalized spectral clustering technique is used to solve the hypergraph partition problem. We compare the proposed method to several methods and its effectiveness is demonstrated by extensive experiments on three image databases.
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image retrieval via probabilistic hypergraph ranking
Computer Vision and Pattern Recognition, 2010Co-Authors: Yuchi Huang, Shaoting Zhang, Dimitris N MetaxasAbstract:In this paper, we propose a new transductive learning framework for image retrieval, in which images are taken as vertices in a weighted hypergraph and the task of image search is formulated as the problem of hypergraph ranking. Based on the similarity matrix computed from various feature descriptors, we take each image as a ‘centroid’ vertex and form a hyperedge by a centroid and its k-nearest neighbors. To further exploit the correlation information among images, we propose a probabilistic hypergraph, which assigns each vertex v i to a hyperedge e j in a probabilistic way. In the incidence structure of a probabilistic hypergraph, we describe both the higher order grouping information and the affinity relationship between vertices within each hy-peredge. After feedback images are provided, our retrieval system ranks image labels by a transductive inference approach, which tends to assign the same label to vertices that share many incidental Hyperedges, with the constraints that predicted labels of feedback images should be similar to their initial labels. We compare the proposed method to several other methods and its effectiveness is demonstrated by extensive experiments on Corel5K, the Scene dataset and Caltech 101.
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video object segmentation by hypergraph cut
Computer Vision and Pattern Recognition, 2009Co-Authors: Yuchi Huang, Dimitris N MetaxasAbstract:In this paper, we present a new framework of video object segmentation, in which we formulate the task of extracting prominent objects from a scene as the problem of hypergraph cut. We initially over-segment each frame in the sequence, and take the over-segmented image patches as the vertices in the graph. Different from the traditional pairwise graph structure, we build a novel graph structure, hypergraph, to represent the complex spatio-temporal neighborhood relationship among the patches. We assign each patch with several attributes that are computed from the optical flow and the appearance-based motion profile, and the vertices with the same attribute value is connected by a hyperedge. Through all the Hyperedges, not only the complex non-pairwise relationships between the patches are described, but also their merits are integrated together organically. The task of video object segmentation is equivalent to the hypergraph partition, which can be solved by the hypergraph cut algorithm. The effectiveness of the proposed method is demonstrated by extensive experiments on nature scenes.
Meng Wang - One of the best experts on this subject based on the ideXlab platform.
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visual classification by ell _1 hypergraph modeling
IEEE Transactions on Knowledge and Data Engineering, 2015Co-Authors: Meng Wang, Xindong WuAbstract:Visual classification has attracted considerable research interests in the past decades. In this paper, a novel $\ell _1$ -hypergraph model for visual classification is proposed. Hypergraph learning, as a natural extension of graph model, has been widely used in many machine learning tasks. In previous work, hypergraph is usually constructed by attribute-based or neighborhood-based methods. That is, a hyperedge is generated by connecting a set of samples sharing a same feature attribute or in a neighborhood. However, these methods are unable to explore feature space globally or sensitive to noises. To address these problems, we propose a novel hypergraph construction approach that leverages sparse representation to generate Hyperedges and learns the relationship among Hyperedges and their vertices. First, for each sample, a hyperedge is generated by regarding it as the centroid and linking it as well as its nearest neighbors. Then, the sparse representation method is applied to represent the centroid vertex by other vertices within the same hyperedge. The vertices with zero coefficients are removed from the hyperedge. Finally, the representation coefficients are used to define the incidence relation between the hyperedge and the vertices. In our approach, we also optimize the hyperedge weights to modulate the effects of different Hyperedges. We leverage the prior knowledge on the Hyperedges so that the Hyperedges sharing more vertices can have closer weights, where a graph Laplacian is used to regularize the optimization of the weights. Our approach is named $\ell _1$ -hypergraph since the $\ell _1$ sparse representation is employed in the hypergraph construction process. The method is evaluated on various visual classification tasks, and it demonstrates promising performance.
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adaptive hypergraph learning and its application in image classification
IEEE Transactions on Image Processing, 2012Co-Authors: Jun Yu, Meng WangAbstract:Recent years have witnessed a surge of interest in graph-based transductive image classification. Existing simple graph-based transductive learning methods only model the pairwise relationship of images, however, and they are sensitive to the radius parameter used in similarity calculation. Hypergraph learning has been investigated to solve both difficulties. It models the high-order relationship of samples by using a hyperedge to link multiple samples. Nevertheless, the existing hypergraph learning methods face two problems, i.e., how to generate Hyperedges and how to handle a large set of Hyperedges. This paper proposes an adaptive hypergraph learning method for transductive image classification. In our method, we generate Hyperedges by linking images and their nearest neighbors. By varying the size of the neighborhood, we are able to generate a set of Hyperedges for each image and its visual neighbors. Our method simultaneously learns the labels of unlabeled images and the weights of Hyperedges. In this way, we can automatically modulate the effects of different Hyperedges. Thorough empirical studies show the effectiveness of our approach when compared with representative baselines.
Yuchi Huang - One of the best experts on this subject based on the ideXlab platform.
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unsupervised image categorization by hypergraph partition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Co-Authors: Yuchi Huang, Fengjun Lv, Yihong Gong, Dimitris N MetaxasAbstract:We present a framework for unsupervised image categorization in which images containing specific objects are taken as vertices in a hypergraph and the task of image clustering is formulated as the problem of hypergraph partition. First, a novel method is proposed to select the region of interest (ROI) of each image, and then Hyperedges are constructed based on shape and appearance features extracted from the ROIs. Each vertex (image) and its k-nearest neighbors (based on shape or appearance descriptors) form two kinds of Hyperedges. The weight of a hyperedge is computed as the sum of the pairwise affinities within the hyperedge. Through all of the Hyperedges, not only the local grouping relationships among the images are described, but also the merits of the shape and appearance characteristics are integrated together to enhance the clustering performance. Finally, a generalized spectral clustering technique is used to solve the hypergraph partition problem. We compare the proposed method to several methods and its effectiveness is demonstrated by extensive experiments on three image databases.
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image retrieval via probabilistic hypergraph ranking
Computer Vision and Pattern Recognition, 2010Co-Authors: Yuchi Huang, Shaoting Zhang, Dimitris N MetaxasAbstract:In this paper, we propose a new transductive learning framework for image retrieval, in which images are taken as vertices in a weighted hypergraph and the task of image search is formulated as the problem of hypergraph ranking. Based on the similarity matrix computed from various feature descriptors, we take each image as a ‘centroid’ vertex and form a hyperedge by a centroid and its k-nearest neighbors. To further exploit the correlation information among images, we propose a probabilistic hypergraph, which assigns each vertex v i to a hyperedge e j in a probabilistic way. In the incidence structure of a probabilistic hypergraph, we describe both the higher order grouping information and the affinity relationship between vertices within each hy-peredge. After feedback images are provided, our retrieval system ranks image labels by a transductive inference approach, which tends to assign the same label to vertices that share many incidental Hyperedges, with the constraints that predicted labels of feedback images should be similar to their initial labels. We compare the proposed method to several other methods and its effectiveness is demonstrated by extensive experiments on Corel5K, the Scene dataset and Caltech 101.
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video object segmentation by hypergraph cut
Computer Vision and Pattern Recognition, 2009Co-Authors: Yuchi Huang, Dimitris N MetaxasAbstract:In this paper, we present a new framework of video object segmentation, in which we formulate the task of extracting prominent objects from a scene as the problem of hypergraph cut. We initially over-segment each frame in the sequence, and take the over-segmented image patches as the vertices in the graph. Different from the traditional pairwise graph structure, we build a novel graph structure, hypergraph, to represent the complex spatio-temporal neighborhood relationship among the patches. We assign each patch with several attributes that are computed from the optical flow and the appearance-based motion profile, and the vertices with the same attribute value is connected by a hyperedge. Through all the Hyperedges, not only the complex non-pairwise relationships between the patches are described, but also their merits are integrated together organically. The task of video object segmentation is equivalent to the hypergraph partition, which can be solved by the hypergraph cut algorithm. The effectiveness of the proposed method is demonstrated by extensive experiments on nature scenes.
Georg Sander - One of the best experts on this subject based on the ideXlab platform.
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Graph Drawing - Layout of Directed Hypergraphs with Orthogonal Hyperedges
Graph Drawing, 2004Co-Authors: Georg SanderAbstract:We present a layout algorithm for directed hypergraphs. A hypergraph contains Hyperedges that have multiple source and target nodes. Hyperedges are drawn with orthogonal segments. Nodes are organized in layers, so that for the majority of Hyperedges the source nodes are placed in a higher layer than the target nodes, similar to traditional hierarchical layout [8,11]. The algorithm was implemented using ILOG JViews [10] for a project that targeted electrical signal visualization.
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layout of directed hypergraphs with orthogonal Hyperedges
Graph Drawing, 2003Co-Authors: Georg SanderAbstract:We present a layout algorithm for directed hypergraphs. A hypergraph contains Hyperedges that have multiple source and target nodes. Hyperedges are drawn with orthogonal segments. Nodes are organized in layers, so that for the majority of Hyperedges the source nodes are placed in a higher layer than the target nodes, similar to traditional hierarchical layout [8,11]. The algorithm was implemented using ILOG JViews [10] for a project that targeted electrical signal visualization.
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Layout of directed hypergraphs with orthogonal Hyperedges (extended abstract)
Lecture Notes in Computer Science, 2003Co-Authors: Georg SanderAbstract:We present a layout algorithm for directed hypergraphs. A hypergraph contains Hyperedges that have multiple source and target nodes. Hyperedges are drawn with orthogonal segments. Nodes are organized in layers, so that for the majority of Hyperedges the source nodes are placed in a higher layer than the target nodes, similar to traditional hierarchical layout [8,11]. The algorithm was implemented using ILOG JViews[10] for a project that targeted electrical signal visualization.
Zhicheng Zhao - One of the best experts on this subject based on the ideXlab platform.
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tag based social image search with Hyperedges correlation
Visual Communications and Image Processing, 2014Co-Authors: Leiquan Wang, Zhicheng ZhaoAbstract:In social image search, most existing hypergraph methods use the visual and textual features in isolation by treating each feature term as a hyperedge. Nevertheless, they neglect the correlations of visual and textual Hyperedges, which are more robust to represent the high-order relationship among vertices. In this paper, we propose a hypergraph with correlated Hyperedges (CHH), which introduces high-order relationship of Hyperedges into hypergraph learning. Based on CHH, a pairwise visual-textual correlation hypergraph (VTCH) model is used for tag-based social image search. To overcome the large number of newly generated hybrid Hyperedges, a bagging-based method is adopted to balance the accuracy and speed. Finally, adaptive Hyperedges learning method is used to obtain the relevance score for social image search. The experiments conducted on MIR Flickr show the effectiveness of our proposed method.
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VCIP - Tag-based social image search with Hyperedges correlation
2014 IEEE Visual Communications and Image Processing Conference, 2014Co-Authors: Leiquan Wang, Zhicheng ZhaoAbstract:In social image search, most existing hypergraph methods use the visual and textual features in isolation by treating each feature term as a hyperedge. Nevertheless, they neglect the correlations of visual and textual Hyperedges, which are more robust to represent the high-order relationship among vertices. In this paper, we propose a hypergraph with correlated Hyperedges (CHH), which introduces high-order relationship of Hyperedges into hypergraph learning. Based on CHH, a pairwise visual-textual correlation hypergraph (VTCH) model is used for tag-based social image search. To overcome the large number of newly generated hybrid Hyperedges, a bagging-based method is adopted to balance the accuracy and speed. Finally, adaptive Hyperedges learning method is used to obtain the relevance score for social image search. The experiments conducted on MIR Flickr show the effectiveness of our proposed method.