Efficient clothing retrieval with semantic-preserving visual phrases

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7725 LNCS (PART 2), pp. 420 - 431
Issue Date:
2013-04-11
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012006403OK.pdf1.91 MB
Adobe PDF
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. © 2013 Springer-Verlag.
Please use this identifier to cite or link to this item: