In recent years, e-commerce has become an important way people shop. Among this, clothes and bags are extraordinarily important for customers. However, traditional online shopping modes only allow users to search with key words. Sometimes users may find it very hard to precisely describe what they want in words. Moreover, even if a user gives a detailed description, it may not agree with the description provided by the seller. Therefore, search-by-image without the help of semantic descriptions becomes a research focus in computer vision and multi-media processing. In this paper, we address the problem of object detection and retrieval and focus particularly on bags in street shots. First, we locate the bag region in an image by Pairwise Context based Convolutional Neural Network PC-CNN. After that, we learn high-level descriptions of bag images based on attributes and build a retrieval system allowing for image search. We test our approach on the publicly available Fashionista Benchmark FB and a Pedestrian with Bags dataset PB collected by ourselves to demonstrate the effectiveness of the proposed method.
['Chong Cao', 'Yuning Du', 'Haizhou Ai']
Bag Detection and Retrieval in Street Shots