WordNet: a lexical database for English
Communications of the ACM
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
How people describe their image information needs: a grounded theory analysis of visual arts queries
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image annotation via graph learning
Pattern Recognition
Proceedings of the 18th international conference on World wide web
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning distance functions for image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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With the development of E-commerce, clothing search on Internet emerges to be a valuable and challenging problem. Compared with the standard image retrieval approach, there are two main difficulties in clothing search. The first is the numerous clothing variation. Another is that people like to search the clothing, which have the same visual elements under the numerous variation. Motivated by Graph Cut method, an approach called word separation method is proposed to map the clothing visual elements to words, which can simultaneously take into account the image-to-image relationship, the image-to-word relationship and the word-to-word relationship. In our work, the meaningful words from web pages are represented by the graph nodes. The graph edges are weighted by the context of data set, which is from Internet. The experimental results on the clothing data set demonstrate the efficiency, effectiveness and robustness of our method.