Efficient Cloth Pattern Recognition Using Random Ferns

Inseong HWANG  Seungwoo JEON  Beobkeun CHO  Yoonsik CHOE  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.2   pp.475-478
Publication Date: 2015/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDL8197
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
circular patch window,  cloth pattern recognition,  delta-HOG,  DHOG,  rotation and scale invariant random ferns,  

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This paper proposes a novel image classification scheme for cloth pattern recognition. The rotation and scale invariant delta-HOG (DHOG)-based descriptor and the entire recognition process using random ferns with this descriptor are proposed independent from pose and scale changes. These methods consider maximun orientation and various radii of a circular patch window for fast and efficient classification even when cloth patches are rotated and the scale is changed. It exhibits good performance in cloth pattern recognition experiments. It found a greater number of similar cloth patches than dense-SIFT in 20 tests out of a total of 36 query tests. In addition, the proposed method is much faster than dense-SIFT in both training and testing; its time consumption is decreased by 57.7% in training and 41.4% in testing. The proposed method, therefore, is expected to contribute to real-time cloth searching service applications that update vast numbers of cloth images posted on the Internet.