The Experts below are selected from a list of 117786 Experts worldwide ranked by ideXlab platform
Yue Gao - One of the best experts on this subject based on the ideXlab platform.
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view based 3 d Model Retrieval a benchmark
IEEE Transactions on Systems Man and Cybernetics, 2018Co-Authors: An-an Liu, Wei Zhi Nie, Yue GaoAbstract:View-based 3-D Model Retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods. Moreover, we concurrently evaluate both hand-crafted visual features and deep features on four popular datasets (NTU60, NTU216, PSB, and ETH) and one challenging real-world multiview Model dataset (MV-RED) prepared by our group with various evaluation criteria to understand how these algorithms perform. By quantitatively analyzing the performances, we discover the graph matching-based method with deep features, especially the clique graph matching algorithm with convolutional neural networks features, can usually outperform the others. We further discuss the future research directions in this field.
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Multi-Modal Clique-Graph Matching for View-Based 3D Model Retrieval
IEEE Transactions on Image Processing, 2016Co-Authors: An-an Liu, Wei Zhi Nie, Yue Gao, Yu Ting SuAbstract:Multi-view matching is an important but a challenging task in view-based 3D Model Retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and hyper-edges that link pairwise cliques. Moreover, we propose an image set-based clique/edgewise similarity measure to address the issue of the set-to-set distance measure, which is the core problem in MCG matching. The proposed MCG provides the following benefits: 1) preserves the local and global attributes of a graph with the designed structure; 2) eliminates redundant and noisy information by strengthening inliers while suppressing outliers; and 3) avoids the difficulty of defining high-order attributes and solving hyper-graph matching. We validate the MCG-based 3D Model Retrieval using three popular single-modal data sets and one novel multi-modal data set. Extensive experiments show the superiority of the proposed method through comparisons. Moreover, we contribute a novel real-world 3D object data set, the multi-view RGB-D object data set. To the best of our knowledge, it is the largest real-world 3D object data set containing multi-modal and multi-view information.
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learning based bipartite graph matching for view based 3d Model Retrieval
IEEE Transactions on Image Processing, 2014Co-Authors: Jinhui Tang, Yue GaoAbstract:Distance measure between two sets of views is one central task in view-based 3D Model Retrieval. In this paper, we introduce a distance metric learning method for bipartite graph matching-based 3D object Retrieval framework. In this method, the relationship among 3D Models is formulated by a graph structure with semisupervised learning to estimate the Model relevance. More specially, we Model two sets of views by using a bipartite graph, on which their optimal matching is estimated. Then, we learn a refined distance metric by using the user’s relevance feedback. The proposed method has been evaluated on four data sets and the experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed method.
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3D Model comparison using spatial structure circular descriptor
Pattern Recognition, 2010Co-Authors: Yue Gao, Qionghai Dai, Naiyao ZhangAbstract:This paper proposes a 3D Model comparison algorithm based on a 3D Model descriptor: spatial structure circular descriptor (SSCD). The spatial structure is important in content-based 3D Model analysis. Within the SSCD, the spatial structure of a 3D Model is described by 2D images, and the attribute values of each pixel represent 3D spatial information. Hence, SSCD can preserve the global spatial structure of 3D Models, and is invariant to rotation and scaling. In addition, by using 2D images to describe the spatial information of 3D Models, all spatial information of the 3D Models can be represented by SSCD without redundancy. Thus, SSCD can be applied to many scenarios which utilize spatial information. In this paper, an SSCD-based 3D Model comparison algorithm is presented. The proposed algorithm has been tested on 3D Model Retrieval experiments. Experimental results demonstrate the effectiveness of the proposed algorithm.
Yu Ting Su - One of the best experts on this subject based on the ideXlab platform.
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Multi-Modal Clique-Graph Matching for View-Based 3D Model Retrieval
IEEE Transactions on Image Processing, 2016Co-Authors: An-an Liu, Wei Zhi Nie, Yue Gao, Yu Ting SuAbstract:Multi-view matching is an important but a challenging task in view-based 3D Model Retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and hyper-edges that link pairwise cliques. Moreover, we propose an image set-based clique/edgewise similarity measure to address the issue of the set-to-set distance measure, which is the core problem in MCG matching. The proposed MCG provides the following benefits: 1) preserves the local and global attributes of a graph with the designed structure; 2) eliminates redundant and noisy information by strengthening inliers while suppressing outliers; and 3) avoids the difficulty of defining high-order attributes and solving hyper-graph matching. We validate the MCG-based 3D Model Retrieval using three popular single-modal data sets and one novel multi-modal data set. Extensive experiments show the superiority of the proposed method through comparisons. Moreover, we contribute a novel real-world 3D object data set, the multi-view RGB-D object data set. To the best of our knowledge, it is the largest real-world 3D object data set containing multi-modal and multi-view information.
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graph based characteristic view set extraction and matching for 3d Model Retrieval
Information Sciences, 2015Co-Authors: Zhongyang Wang, Yu Ting SuAbstract:In recent times, multi-view representation of the 3D Model has led to extensive research in view-based methods for 3D Model Retrieval. However, most approaches focus on feature extraction from 2D images while ignoring the spatial information of the 3D Model. In order to improve the effectiveness of view-based methods on 3D Model Retrieval, this paper proposes a novel method for characteristic view extraction and similarity measurement. First, the graph clustering method is used for view grouping and the random-walk algorithm is applied to adaptively update the weight of each view. The spatial information of the 3D object is utilized to construct a view-graph Model, thus enabling each characteristic view to represent the discriminative visual feature in terms of specific spatial context. Next, by considering the view set as a graph Model, the similarity measurement of two Models can be converted into a graph matching problem. This problem is solved by mathematically formulating it as a Rayleigh quotient maximization with affinity constraints for similarity measurement. Extensive comparison experiments were conducted on the popular ETH, NTU, PSB, and MV-RED 3D Model datasets. The results demonstrate the superiority of the proposed method.
Naiyao Zhang - One of the best experts on this subject based on the ideXlab platform.
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3d Model Retrieval using weighted bipartite graph matching
Signal Processing-image Communication, 2011Co-Authors: Meng Wang, Naiyao ZhangAbstract:In this paper, we propose a view-based 3D Model Retrieval algorithm, where many-to-many matching method, weighted bipartite graph matching, is employed for comparison between two 3D Models. In this work, each 3D Model is represented by a set of 2D views. Representative views are first selected from the query Model and the corresponding initial weights are provided. These initial weights are further updated based on the relationship among these representative views. The weighted bipartite graph is built with these selected 2D views, and the matching result is used to measure the similarity between two 3D Models. Experimental results and comparison with existing methods show the effectiveness of the proposed algorithm.
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3D Model comparison using spatial structure circular descriptor
Pattern Recognition, 2010Co-Authors: Yue Gao, Qionghai Dai, Naiyao ZhangAbstract:This paper proposes a 3D Model comparison algorithm based on a 3D Model descriptor: spatial structure circular descriptor (SSCD). The spatial structure is important in content-based 3D Model analysis. Within the SSCD, the spatial structure of a 3D Model is described by 2D images, and the attribute values of each pixel represent 3D spatial information. Hence, SSCD can preserve the global spatial structure of 3D Models, and is invariant to rotation and scaling. In addition, by using 2D images to describe the spatial information of 3D Models, all spatial information of the 3D Models can be represented by SSCD without redundancy. Thus, SSCD can be applied to many scenarios which utilize spatial information. In this paper, an SSCD-based 3D Model comparison algorithm is presented. The proposed algorithm has been tested on 3D Model Retrieval experiments. Experimental results demonstrate the effectiveness of the proposed algorithm.
Jinhui Tang - One of the best experts on this subject based on the ideXlab platform.
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learning based bipartite graph matching for view based 3d Model Retrieval
IEEE Transactions on Image Processing, 2014Co-Authors: Jinhui Tang, Yue GaoAbstract:Distance measure between two sets of views is one central task in view-based 3D Model Retrieval. In this paper, we introduce a distance metric learning method for bipartite graph matching-based 3D object Retrieval framework. In this method, the relationship among 3D Models is formulated by a graph structure with semisupervised learning to estimate the Model relevance. More specially, we Model two sets of views by using a bipartite graph, on which their optimal matching is estimated. Then, we learn a refined distance metric by using the user’s relevance feedback. The proposed method has been evaluated on four data sets and the experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed method.
Weiguo Pan - One of the best experts on this subject based on the ideXlab platform.
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3d Model Retrieval and classification by semi supervised learning with content based similarity
Information Sciences, 2014Co-Authors: Qian Wang, Jian Xue, Weiguo PanAbstract:The rapid development of 3D digital technology has led to an increasing volume of 3D Model data. In addressing the management of such large scale data, effective content-based 3D Model Retrieval and recognition methods are highly desirable. In 3D Model Retrieval and recognition tasks, the distance measure between two 3D Models plays an important role. In this paper, we propose a novel 3D Model Retrieval and recognition method that employs both a distance histogram and 3D moment invariants as features that are invariant to 3D object scaling, translation, and rotation. Disjoint information is used to measure the distance between the feature histograms, and the Euclidean distance is applied in calculating the distance between two moment features. These measures are then combined as the 3D Model distance. Using this distance measure, the relationships between all 3D Models in the dataset are formulated as a graph structure. A semi-supervised learning process is then conducted to estimate the relevance among the 3D Models, and this is employed for 3D Model Retrieval and classification. To evaluate the effectiveness of the proposed method, we conduct experiments on two datasets. Experimental results and a comparison with state-of-the-art methods demonstrate that the proposed method achieves improved performance for 3D Model Retrieval and recognition tasks.