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Efficient batch similarity join processing of social images based on arbitrary features

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Abstract

In this paper, we identify and solve a multi-join optimization problem for Arbitrary Feature-based social image Similarity JOINs(AFS-JOIN). Given two collections(i.e., R and S) of social images that carry both visual, spatial and textual(i.e., tag) information, the multiple joins based on arbitrary features retrieves the pairs of images that are visually, textually similar or spatially close from different users. To address this problem, in this paper, we have proposed three methods to facilitate the multi-join processing: 1) two baseline approaches(i.e., a naïve join approach and a maximal threshold(MT)-based), and 2) a Batch Similarity Join(BSJ) method. For the BSJ method, given m users’ join requests, they are first conversed and grouped into m″ clusters which correspond to m″ join boxes, where m > m″. To speedup the BSJ processing, a feature distance space is first partitioned into some cubes based on four segmentation schemes; the image pairs falling in the cubes are indexed by the cube tree index; thus BSJ processing is transformed into the searching of the image pairs falling in some affected cubes for m″ AFS-JOINs with the aid of the index. An extensive experimental evaluation using real and synthetic datasets shows that our proposed BSJ technique outperforms the state-of-the-art solutions.

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Notes

  1. Note that, the total number of features in the AFS-JOIN is three (e.g., visual, textual and spatial features) in this paper, it can be easily extended to support multiple features AFS-JOINs such as textual, shape and temporal features, etc.

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Acknowledgments

This paper is partially supported by the Program of National Natural Science Foundation of China under Grant No. 61272188; the Program of Natural Science Foundation of Zhejiang Province under Grant Nos. LY13F020008, and LY13F020010; the Ministry of Education of Humanities and Social Sciences Project under Grant No. 14YJCZH235. National Center for International Joint Research on E-Business Information Processing (2013B01035).

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Authors and Affiliations

  1. College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, People’s Republic of China

    Yi Zhuang & Chunhua Ju

  2. Hangzhou First People’s Hospital, Hangzhou, People’s Republic of China

    Nan Jiang

  3. Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, People’s Republic of China

    Zhi-Ang Wu & Jie Cao

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  1. Yi Zhuang
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  2. Nan Jiang
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  5. Chunhua Ju
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Correspondence to Yi Zhuang.

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Zhuang, Y., Jiang, N., Wu, ZA. et al. Efficient batch similarity join processing of social images based on arbitrary features. World Wide Web 19, 725–753 (2016). https://doi.org/10.1007/s11280-015-0355-z

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