Improve Pedestrian Attribute Classification by Weighted Interactions from Other Attributes

Recent works have shown that visual attributes are useful in a number of applications, such as object classification, recognition, and retrieval. However, predicting attributes in images with large variations still remains a challenging problem. Several approaches have been proposed for visual attribute classification; however, most of them assume independence among attributes. In fact, to predict one attribute, it is often useful to consider other related attributes. For example, a pedestrian with long hair and skirt usually imply the female attribute. Motivated by this, we propose a novel pedestrian attribute classification method which exploits interactions among different attributes. Firstly, each attribute classifier is trained independently. Secondly, for each attribute, we also use the decision scores of other attribute classifiers to learn the attribute interaction regressor. Finally, prediction of one attribute is achieved by a weighted combination of the independent decision score and the interaction score from other attributes. The proposed method is able to keep the balance of the independent decision score and interaction of other attributes to yield more robust classification results. Experimental results on the Attributed Pedestrian in Surveillance (APiS 1.0) [1] database validate the effectiveness of the proposed approach for pedestrian attribute classification.