基于衣物共现信息与多任务学习的衣物识别<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201606007

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Abstract Multi-task learning (MTL) ignores the influence of prior probability on the process of learning. Aiming at this problem, an approach for clothing recognition based on MTL and the co-occurrence information of the clothes categories (CA-MTL) is proposed. The prior constraint term is incorporated into the MTL model to integrate the co-occurrence information of the clothes categories. The original extended gradient method is modified correspondingly and the performance of the clothing classifiers is thus enhanced. Experimental results show that the average performance of CA-MTL outperforms those of single task learning, neural network and traditional multi-task learning. Furthermore, the training results of the proposed model are convenient for visualization and can be used for feature selection.

Key words： Clothes Recognition Multi-task Learning (MTL) Clothes Co-occurrence Constraint

Received: 08 October 2015

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	Articles by authors
	HAO Zhifeng
	LING Suiyi
	WEN Wen
	CAI Ruichu
	YUAN Chang

Cite this article:

HAO Zhifeng,LING Suiyi,WEN Wen等. Clothing Recognition Based on Clothing Co-occurrence Information and Multi-task Learning[J]. , 2016, 29(6): 534-541.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201606007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I6/534

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