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Yin, Qiyue; Wu, Shu; Wang, Liang
Conference NameInternational Conference on Image Processing
Source PublicationIn Proceedings of the International Conference on Image Processing (ICIP), 2015
Conference DateSep. 27-30
Conference PlaceQuébec
AbstractWith the exponential growth of tagged images, researchers are resorting to this high semantic tag information to assist the clustering process and promising clustering results have been obtained. However, users may not tag all of their images or some of the images are partially annotated, and this will lead to big performance degradation, which is rarely considered by pervious works. To alleviate this problem, we propose a new framework for image clustering assisted by partially observed tags. Our model enforces the sparse representation obtained through sparse coding and the latent tag representation learned via matrix factorization to be consistent with the partial image-tag observations. Finally, the partitioning of the database is performed using clustering algorithms (e.g., kmeans) on the sparse representation. Extensive experiments on three real world datasets demonstrate that the proposed model performs better than the state-of-the-art methods.
KeywordImage Clustering Multi-view Clustering Sparse Coding
Document Type会议论文
Corresponding AuthorWu, Shu
Recommended Citation
GB/T 7714
Yin, Qiyue,Wu, Shu,Wang, Liang. PARTIALLY TAGGED IMAGE CLUSTERING[C],2015.
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