Intraclass Identifiable Region Detection

Abstract

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.