Automatic clothing recommendation grows dramatically due to the booming of apparel e-commerce. In this paper, we propose a novel clothing recommendation approach which is sensitive to the fashion trend. The proposed approach incorporates the expert knowledge into multiple dimensional information including purchase behaviors, image contents and product descriptions so as to provide recommendation of clothing in line with the forefront of fashion. Meanwhile, to meet with human visual aesthetics and user's collocation experience, we propose the integration of the convolutional neural network and the hierarchical collocation model (HCM) into our framework. The former is to extract effective visual features and attribute descriptors from the clothing items, while the latter embeds them into the concept of style topics which interpret the collocation pattern from a higher level of semantic knowledge. Such a data driven recommendation approach is able to learn clothing collocation metric from multi-dimensional clothing information. Experimental results show that our HCM method achieves better performance than other state-of-the-art baselines. Besides, it also ensures the fashion sensitivity of the recommended outfits.

This demo delivers a novel retrieval system which meets users' multi-dimensional requirements in clothing image search. In this system, users are able to use both image and keywords as query inputs. We employ the color, texture, shape and attributes as additional descriptors to further refine the requirements. We propose the Hybrid Topic (HT) model, a probabilistic network integrating the multi-channel descriptors into a unified framework, to learn the intricate semantic representation of the descriptors above. The proposed model provides an effective multi-modal representation of clothes. Our experiments show that the HT method significantly outperforms the CNN-based deep search methods.

This paper proposes a novel approach to meet users' multi-dimensional requirements in clothing image retrieval. It enables users to add search conditions by modifying the color, texture, shape and attribute descriptors of the query images to further refine their requirements. We propose the Hybrid Topic (HT) model to learn the intricate semantic representation of the descriptors above. The model provides an effective multi-dimensional representation of clothes and is able to perform automatic image annotation by probabilistic reasoning from image search. Furthermore, we develop a demand-adaptive retrieval strategy which refines users' specific requirements and removes users' unwanted features. Our experiments show that the HT method significantly outperforms the deep neural network methods. The accuracy could be further improved in cooperation with image annotation and demand-adaptive retrieval strategy.