Brand-Aware Fashion Clothing Search using CNN Feature Encoding and Re-ranking

Brand plays a significant role in fashion clothing. Consumers are brand conscious during the clothing search and purchase. Existing visual fashion search methods [1], [8], [17]–[19] often do not explicitly consider the brand information such as logos. Brand logo in clothing images are quite small and often suffer various deformations, and hence pose a significant challenge for branded clothing search. In view of this, this paper presents a new Brand-Aware Fashion Search (BAFS) framework that explores the brand information during the visual search. We construct a new brand fashion dataset which consists of 10K images of branded clothing with trademark logos. The proposed framework first jointly detects the brand logo and clothing items in the images. Next, to extract the rich visual information from the clothing images, we propose a new deep feature encoding known as Principal Component Maximum Activation of Convolutions (PMAC) that leverages hierarchies of CNN activations. The PMAC feature aims to capture both low-level visual information and high-level global abstraction from the images. The proposed method further uses a brand-aware re-ranking technique to improve the search. Experiments conducted on the brand fashion dataset shows that the proposed framework achieves superior performance to other comparative methods.