CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Efficient Clothing Retrieval with Semantic-Preserving Visual Phrases
Fu, Jianlong; Wang, Jinqiao; Li, Zechao; Xu, Min; Lu, Hanqing
2012
Conference NameACCV2012
Source PublicationAsian Conference on Computer Vision (ACCV)
Pages420-431
Conference Date2012
Conference PlaceDaejeon, Korea
AbstractIn this paper, we address the problem of large scale crossscenario
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.
KeywordSemantic-preserving Visual Phrases Efficient Clothing Retrieval
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/4685
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorWang, Jinqiao
Recommended Citation
GB/T 7714
Fu, Jianlong,Wang, Jinqiao,Li, Zechao,et al. Efficient Clothing Retrieval with Semantic-Preserving Visual Phrases[C],2012:420-431.
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