Finding suits in images of people in unconstrained environments

• Published in: Journal of visual communication and image representation Vol. 25; no. 7; pp. 1588 - 1594
• Main Authors: Yan, Chenggang Clarence, Huang, Lei, Wei, Zhiqiang, Nie, Jie, Chen, Bochuan, Zhang, Yingping
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2014
• Subjects: Clothing style; Color; Color feature; Human; Image detection; People search; Photo ranking; Representations; Shape feature; Statistical feature; Suits detection; Tasks; Understanding image of people; Visual; Shape feature; Clothing style; People search; Understanding image of people; Suits detection; Photo ranking; Statistical feature; Color feature
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2014.07.002

•The main direction of human body is introduced in order to cope with various human poses.•We propose three novel kinds of features, i.e., color features, shape features and statistical features.•We try to address the suit detection issue for images of people in unconstrained environments. Clothing style analysis is a critical step for understanding images of people. To automatically identify the style of clothing that people wear is a challenging task due to various poses of person and large variations for even the same clothing category. Suit as one of the clothing style is a key element in many important activities. In this paper, we propose a novel suits detection method for images of people in unconstrained environments. In order to cope with various human poses, human pose estimation is incorporated. By analyzing the style of clothing, we propose the color features, shape features and statistical features for suits detection. Experiments with four popular classifiers have been conducted to demonstrate that the proposed features are effective and robust. Comparative experiments with Bag of Words (BoW) method demonstrate that the proposed features are superior to BoW which is a popular method for object detection. The proposed method has achieved promising performance over our dataset, which is a challenging web image set with various human poses and diverse styles of clothing.