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http://dx.doi.org/10.5909/JBE.2014.19.3.396

Invariant Classification and Detection for Cloth Searching  

Hwang, Inseong (Yonsei University Electrical and Electronic Eng.)
Cho, Beobkeun (Yonsei University Electrical and Electronic Eng.)
Jeon, Seungwoo (Yonsei University Electrical and Electronic Eng.)
Choe, Yunsik (Yonsei University Electrical and Electronic Eng.)
Publication Information
Journal of Broadcast Engineering / v.19, no.3, 2014 , pp. 396-404 More about this Journal
Abstract
The field of searching clothing, which is very difficult due to the nature of the informal sector, has been in an effort to reduce the recognition error and computational complexity. However, there is no concrete examples of the whole progress of learning and recognizing for cloth, and the related technologies are still showing many limitations. In this paper, the whole process including identifying both the person and cloth in an image and analyzing both its color and texture pattern is specifically shown for classification. Especially, deformable search descriptor, LBPROT_35 is proposed for identifying the pattern of clothing. The proposed method is scale and rotation invariant, so we can obtain even higher detection rate even though the scale and angle of the image changes. In addition, the color classifier with the color space quantization is proposed not to loose color similarity. In simulation, we build database by training a total of 810 images from the clothing images on the internet, and test some of them. As a result, the proposed method shows a good performance as it has 94.4% matching rate while the former Dense-SIFT method has 63.9%.
Keywords
informal; cloth; pattern; descriptor; classification;
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