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CISC: clustered image search by conceptualization

Published: 18 March 2013 Publication History

Abstract

Clustering of images from search results can improve the user experience of image search. Most of the existing systems use both visual features and surrounding texts as signals for clustering while this paper demonstrates the use of an external knowledge base to make better sense out of the text signals in a prototype system called CISC. Once we understand the semantics of the text better, the result of the clustering is significantly improved. In addition to clustering the images by their semantic entities, our system can also conceptualize each image cluster into a set of concepts to represent the meaning of the cluster.

References

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S. Cucerzan. Large-scale named entity disambiguation based on wikipedia data. In Proceedings of EMNLP-CoNLL, volume 2007, pages 708--716, 2007.
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B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:2007, 2007.
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Z. Fu, H. H.-S. Ip, H. Lu, and Z. Lu. Multi-modal constraint propagation for heterogeneous image clustering. In ACM Multimedia, pages 143--152, 2011.
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S. hua Zhong, Y. Liu, and Y. Liu. Bilinear deep learning for image classification. In ACM Multimedia, pages 343--352, 2011.
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L.-J. Li, R. Socher, and F.-F. Li. Towards total scene understanding: Classification, annotation and segmentation in an automatic framework. In CVPR, pages 2036--2043, 2009.

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  1. CISC: clustered image search by conceptualization

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    cover image ACM Other conferences
    EDBT '13: Proceedings of the 16th International Conference on Extending Database Technology
    March 2013
    793 pages
    ISBN:9781450315975
    DOI:10.1145/2452376

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 March 2013

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    Author Tags

    1. clustering
    2. conceptualization
    3. image search

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    EDBT/ICDT '13

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    Overall Acceptance Rate 7 of 10 submissions, 70%

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