Title: Robust and effective clothes recognition system based on fusion of Haralick and HOG features

Authors: Kriti Bansal; Anand Singh Jalal

Addresses: Computer Engineering and Applications, GLA University, Mathura-281406, India ' Computer Engineering and Applications, GLA University, Mathura-281406, India

Abstract: In today's modern era, when the computer has become a necessity of an individual, shopping has shifted from shop to online shopping. This kind of clothes classification is used for knowing the name of the cloth that we have seen any movie, serial or anywhere else. In this paper, we present an efficient method to recognise the clothes in natural scenes as well as in the cluttered background. The proposed approach includes three phases: extraction of region of interest (ROI); construction of feature vector; classification. We have validated the proposed approach using our dataset which contains cluttered background images as well as on deep fashion standard dataset. The proposed method successfully resolved the issues of misclassification of clothes in the cluttered background with different illumination conditions. Experimental results show that the proposed technique successfully achieved 88.36% clothes recognition rate.

Keywords: clothes recognition; HOG; histogram of oriented gradients; Haralick.

DOI: 10.1504/IJSISE.2019.100649

International Journal of Signal and Imaging Systems Engineering, 2019 Vol.11 No.5, pp.288 - 299

Received: 11 Jan 2018
Accepted: 04 Jul 2018
Published online: 06 Jul 2019 *

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