Abstract
In this paper, we propose a new approach for large scale daily clothing retrieval. Fast clothing image search in cross scenarios is a challenging task due to the large amount of clothing images on the internet and visual differences between street photos (pictures of people wearing clothing taken in our daily life with complex background) and online shop photos (pictures of clothing items on people, captured by professionals in more controlled settings). We tackle the problem of cross-scenario clothing retrieval through clothing segmentation based on coarse-fine hierarchical superpixel segmentation and pose estimation to remove the background of clothing image and employ deep features representing the clothing item aimed at describing various clothing effectively. In addition, in order to speed up the retrieval process for large scale online clothing images, we adopt inverted indexing on deep feature by regarding deep features as Bag-of-Word model. In this way, we obtain similar clothing items far faster. Experiments demonstrate that our method significantly outperforms state-of-the-art approaches.
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Acknowledgment
The authors would like to thank the support of National Natural Science Foundation of China, the Scientific Research Foundation for the Excellent Middle-Aged and Youth Scientists of Shandong Province of China.
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Li, Z., Li, Y., Gao, Y., Liu, Y. (2016). Fast Cross-Scenario Clothing Retrieval Based on Indexing Deep Features. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_11
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