Rapid Clothing Retrieval via Deep Learning of Binary Codes and Hierarchical Search

This paper deals with the problem of clothing retrieval in a recommendation system. We develop a hierarchical deep search framework to tackle this problem. We use a pre-trained network model that has learned rich mid-level visual representations in module 1. Then, in module 2, we add a latent layer to the network and have neurons in this layer to learn hashes-like representations while fine-tuning it on the clothing dataset. Finally, module 3 achieves fast clothing retrieval using the learned hash codes and representations via a coarse-to-fine strategy. We use a large clothing dataset where 161,234 clothes images are collected and labeled. Experiments demonstrate the potential of our proposed framework for clothing retrieval in a large corpus.