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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Efficient Clothing Retrieval with Semantic-Preserving Visual Phrases
verfasst von : Jianlong Fu, Jinqiao Wang, Zechao Li, Min Xu, Hanqing Lu
Erschienen in: Computer Vision – ACCV 2012
Verlag: Springer Berlin Heidelberg
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In this paper, we address the problem of large scale cross-scenario clothing retrieval with semantic-preserving visual phrases (SPVP). Since the human parts are important cues for clothing detection and segmentation, we firstly detect human parts as the semantic context, and refine the regions of human parts with sparse background reconstruction. Then, the semantic parts are encoded into the vocabulary tree under the bag-of-visual-word (BOW) framework, and the contextual constraint of visual words among different human parts is exploited through the SPVP. Moreover, the SPVP is integrated into the inverted index structure for accelerating the retrieval process. Experiments and comparisons on our clothing dataset indicate that the SPVP significantly enhances the discriminative power of local features with a slight increase of memory usage or runtime consumption compared to the BOW model. Therefore, the approach is superior to both the state-of-the-art approach and two clothing search engines.