Fashion analysis with a subordinate attribute classification network
In this paper we deal with two image-based object search tasks in the fashion domain, clothing attribute prediction and cross-domain shoe retrieval. Clothing attribute prediction is about describing the appearances of clothes via semantic attributes and cross-domain shoe retrieval aims at retrieving the same shoe items from online stores given a daily life shoe photo. We jointly solve these two problems by a novel Subordinate Attribute Convolutional Neural Network (SA-CNN), with the newly designed loss function that systematically merges semantic attributes of closer visual appearance to prevent images with obvious visual differences being confused with each other. A three-level feature representation is further developed based on SA-CNN for shoes from different domains. The experimental results demonstrate that the clothing attribute prediction using the proposed SA-CNN achieves better performance than that using traditional features and fine-tuned conventional CNN. Moreover, for the task of cross-domain shoe retrieval, the top-20 retrieval accuracy with deep features extracted from SA-CNN has a significant improvement of 43% compared to that with the pretrained CNN features.