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CONTOUR COVARIANCE: A FAST DESCRIPTOR FOR CLASSIFICATION

Citation Author(s):
Xiaohan Yu, Shengwu Xiong, Yongsheng Gao, Xiaohui Yuan
Submitted by:
Xiaohan Yu
Last updated:
16 September 2019 - 9:00pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Xiaohan Yu
Paper Code:
2598

Abstract

This paper presents a novel shape descriptor to effectively and efficiently characterize the local image statistics. The proposed descriptor, termed contour covariance (CC), characterizes covariance features driven by a moving point on the shape contour at multiple scales. To calculate the covariance matrices, three basic features including texture, intensity and distance map, are extracted from the object image. Based on coefficients of the obtained covariance matrices, the proposed CC descriptor is compact yet informative, as well as invariant to rotation, translation and scale. The experimental results on two databases demonstrate the superiority and efficiency of the proposed method among the state-of-the-art methods in shape classification.

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