Cross-scenario clothing retrieval and fine-grained style recognition

In this paper, we propose a new approach for cross-scenario clothing retrieval and fine-grained clothing style recognition. The query clothing photos captured by cameras or other mobile devices are filled with noisy background while the product clothing images online for shopping are usually presented in a pure environment. We tackle this problem by two steps. Firstly, a hierarchical super-pixel merging algorithm based on semantic segmentation is proposed to obtain the intact query clothing item. Secondly, aiming at solving the problem of clothing style recognition in different scenarios, we propose sparse coding based on domain-adaptive dictionary learning to improve the accuracy of the classifier and adaptability of the dictionary. In this way, we obtain fine-grained attributes of the clothing items and use the attributes matching score to re-rank the retrieval results further. The experiment results show that our method outperforms the state-of-the-art approaches. Furthermore, we build a well labeled clothing dataset, where the images are selected from 1.5 billion product clothing images.