会议专题

Shape Recognition by Combining Contour and Skeleton into a Mid-Level Representation

  Contour and skeleton are two main stream representations for shape recognition in the literature.It has been shown that such two representations convey complementary information,however combining them in a nature way is nontrivial,as they are generally abstracted by different structures (closed string vs graph),respectively.This paper aims at addressing the shape recognition problem by combining contour and skeleton into a mid-level of shape representation.To form a midlevel representation for shape contours,a recent work named Bag of Contour Fragments (BCF) is adopted; While for skeleton,a new midlevel representation named Bag of Skeleton Paths (BSP) is proposed,which is formed by pooling the skeleton codes by encoding the skeleton paths connecting pairs of end points in the skeleton.Finally,a compact shape feature vector is formed by concatenating BCF with BSP and fed into a linear SVM classifier to recognize the shape.Although such a concatenation is simple,the SVM classifier can automatically learn the weights of contour and skeleton features to offer discriminative power.The encouraging experimental results demonstrate that the proposed new shape representation is effective for shape classification and achieves the state-of-the-art performances on several standard shape benchmarks.

Shape Recognition Mid-level Shape Representation Bag of Contour Fragments Bag of Skeleton Paths

Wei Shen Xinggang Wang Cong Yao Xiang Bai

School of Communication and Information Engineering,Shanghai University,149 Yanchang Road,Shanghai 2 Dept. of Electronics and Information Engineering,Huazhong University of Science and Technology,1037

国际会议

Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

长沙

英文

391-400

2014-11-01(万方平台首次上网日期,不代表论文的发表时间)