Intraclass Identifiable Region Detection
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概要
- 論文の詳細を見る
In this paper, we propose a novel method for comparing the shape of similar objects. From the viewpoint of linear algebra, we turn this identifiable region detection problem into a low-rank submatrices searching process, and solve it with biclustering. Comparing with traditional cluster analysis, our method looks for structural information on both object index and local shape dimensions, which leads to more detailed local comparison results. The proposed method is evaluated with real world data with satisfactory results, which verifies the effectiveness of our method.
- Information and Media Technologies 編集運営会議の論文
著者
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IKEUCHI Katsushi
The University of Tokyo
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Zheng Bo
The University of Tokyo
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Takamatsu Jun
Nara Institute of Science and Technology
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Lu Min
The University of Tokyo
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