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Content-Irrelevant Tag Cleansing via Bi-Layer Clustering and Peer Cooperation

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Abstract

User-provided tags for social images have facilitated many fields, such as social image organization, summarization and retrieval. Since the users utilize their own knowledge and personalized language to describe the visual content of social images, these social tags are too imprecise and ambiguous to exploit the social image tagging. In this paper, we discover the content-similar images (peers) and leverage the relationships among these images (peer cooperation) to handle the problem of content-irrelevant tags. A bi-layer clustering framework for discovering content-similar images is proposed to divide image collection into different groups, and the tags of peers in these groups are cleaned jointly based on tag statistics and relevance. The relevance of tags measured by Google Distance is used to generate the first-layer clustering and then the bi-modality similarity of images is used to perform the second-layer clustering. Based on the bi-layer clustering, we utilize peers in a group to identify their content-irrelevant tags. Finally, an extended Fisher’s criterion is proposed to decide the proper number of content-irrelevant tags. To verify the effectiveness of our proposed technique, we conduct the experiments on the social images of Flickr and the standard benchmark. The comparison experiments show that our proposed algorithm achieves positive results for tag cleansing and image retrieval.

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Notes

  1. In [11], authors use the Google search engine and name the approach with ‘Google distance’. We still use the same name even if we utilize the BING search engine and other database in this paper.

  2. The NMF method constructs the estimated image-tag matrix V ′ using the matrix factorization V≈W H (V is the original image-tag matrix). Most zero entities in V would be updated to non-zero by updating W and H through minimizing the objective function F= ∑i=1n ∑j=1m[V i j l o g(W H) i j −(W H) i j ].

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Acknowledgments

This work is partly supported by the doctorate foundation of Northwestern Polytechnical University (Grant No: CX201113), Doctoral Program of Higher Education of China (Grant No. 201161 02110027) and National Natural Science Foundation of China (under Grant No.61075014).

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Zhaoqiang Xia, Xiaoyi Feng & Jinye Peng

  2. University of North Carolina at Charlotte, Charlotte, NC, USA

    Jianping Fan

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  1. Zhaoqiang Xia
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  2. Xiaoyi Feng
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Correspondence to Zhaoqiang Xia.

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Xia, Z., Feng, X., Peng, J. et al. Content-Irrelevant Tag Cleansing via Bi-Layer Clustering and Peer Cooperation. J Sign Process Syst 81, 29–44 (2015). https://doi.org/10.1007/s11265-014-0895-y

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