Attribute Based Approach for Clothing Recognition

摘要：

　　Clothing recognition is hot topic for its potential benefits to lots of visual tasks,such as people identification,pose estimation and recommendation system.However,due to the wide variations of clothing appearance and the semantic gap between low-level features and high-level category concepts,clothing recognition is very challenging.To narrow this gap,a novel method,which uses intermediate attributes to bridge low-level features and high-level category labels,is proposed.This method first recognizes local attributes from low-level visual features,and then infers clothing category based on these attributes.To this end,DPM models and pixel-level parsing are applied to obtain geometric structure attributes,such as collar shape,and geometric size attributes,such as sleeve length,respectively.Then,Multiple Output Neural Networks are built to predict clothing category based on attributes.Experiments show that the performance of our method is superior to two stateof-the-art approaches on both of attribute and category recognition.

关键词： Attribute recognition Clothing recognition Attribute based

作者: Wang Fan Zhao Qiyang Liu Qingjie Yin Baolin

作者单位: State Key Laboratory of Software Development Environment,Beihang University,Beijing,China Intelligent Recognition and Image Processing Lab,Beihang University,Beijing,China

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 364-378

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）