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Android Oriented Image Visualization Exploratory Search

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Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

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Abstract

When using traditional image search engines, smartphone users often complain about their poor user interface including poor user experience, and weak interaction. Moreover, users are unable to find a desired picture partly due to the unclear key words. This paper proposes the word-bag co-occurrence scheme by defining the correlation between images. Through exploratory search, the search range can be expanded and help users refine retrieval of the expected images. Firstly, the proposed scheme applied the bag of visual words (BoVW) vector by processing images on Hadoop. Secondly, similarity matrix was constructed to organize the image data. Finally, the images in which users were interested was visually displayed on the android mobile phone via exploratory search. Comparing the proposed method to current methods by testing with image data sets on ImageNet, the experimental results show that the former is superior to the latter on visual representation, and the proposed scheme can provide a better user experience.

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References

  1. Beaver, D., Kumar, S., Li, H.C., et al.: Finding a needle in Haystack: Facebook’s photo storage. In: Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation, OSDI 2010, pp. 47–60. USENIX Association, Berkeley (2010)

    Google Scholar 

  2. Temple, K.: What Happens In An Internet Minute? [EB/OL]. http://scoop.intel.com/what-happens-in-an-internet-minute. Accessed 13 Mar 2012/1 May 2014

  3. Cai, D., He, X., Li, Z., et al.: Hierarchical clustering of WWW image search results using visual, textual and link information. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, pp. 952–959. ACM (2004)

    Google Scholar 

  4. Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)

    Article  Google Scholar 

  5. Jing, Y., Rowley, H., Wang, J., et al.: Google image swirl: a large-scale content-based image visualization system. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 539–540. ACM, New York (2012)

    Google Scholar 

  6. Yee, K.P., Swearingen, K., Li, K., et al.: Faceted metadata for image search and browsing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 401–408. ACM, New York (2003)

    Google Scholar 

  7. Liu, D., et al.: SmartAdP: visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans. Vis. Comput. Graph. 23(1), 1–10 (2017)

    Article  Google Scholar 

  8. Mukherjea, S., Hirata, K., Hara, Y.: Using clustering and visualization for refining the results of a WWW image search engine. In: Proceedings of the 1998 Workshop on New Paradigms in Information Visualization and Manipulation, pp. 29–35. ACM, New York (1998)

    Google Scholar 

  9. Pujol, J.M., Sangüesa, R., Bermúdez, J.: Porqpine: a distributed and collaborative search engine. In: Poster at the 12th International World Wide Web Conference WWW 2003, Budapest, Hungary (2003)

    Google Scholar 

  10. Westlund, O., Gomez-Barroso, J.L., Compañó, R., et al.: Exploring the logic of mobile search. Behav. Inf. Technol. 30(5), 691–703 (2011)

    Article  Google Scholar 

  11. White, R.W., Kules, B., Bederson, B.: Exploratory search interfaces: categorization, clustering and beyond: report on the XSI 2005 workshop at the Human-Computer Interaction Laboratory, University of Maryland. In: ACM SIGIR Forum, vol. 39, no. 2, pp. 52–56. ACM (2005)

    Google Scholar 

  12. White, R.W., Roth, R.A.: Exploratory search: beyond the query-response paradigm. Synth. Lect. Inf. Concepts Retr. Serv. 1(1), 1–98 (2009)

    Google Scholar 

  13. Klouche, K., Ruotsalo, T., Cabral, D., Andolina, S., Bellucci, A., Jacucci, G.: Designing for exploratory search on touch devices. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 4189–4198, April 2015

    Google Scholar 

  14. Rui, Y., Huang, T.S., Ortega, M., et al.: Relevance feedback: a powerful tool for interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Technol. 8(5), 644–655 (1998)

    Article  Google Scholar 

  15. Suditu, N., Fleuret, F.: HEAT: iterative relevance feedback with one million images. In: International Conference on Computer Vision, pp. 2118–2125. IEEE Computer Society (2011)

    Google Scholar 

  16. Mohanan, A., Raju, S.: A Survey on different relevance feedback techniques in content based image retrieval. Int. Res. J. Eng. Technol. (IRJET) 04(02), 582–585 (2017)

    Google Scholar 

  17. Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 524–531. IEEE (2005)

    Google Scholar 

  18. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  19. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering analysis and an algorithm. In: Proceedings of Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press, Cambridge (2001)

    Google Scholar 

  20. Hare, J.S., Samangooei, S., Dupplaw, D.P.: OpenIMAJ and ImageTerrier: Java libraries and tools for scalable multimedia analysis and indexing of images. ACM (2011)

    Google Scholar 

  21. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems (NIPS). MIT Press, Cambridge (2004)

    Google Scholar 

  22. Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)

    Google Scholar 

  23. Smith, J.R.: Image retrieval evaluation. In: IEEE Workshop on Content-based Access of Image and Video Libraries, pp. 112–113 (1998). http://www.ee.columbia.edu/~jrsmith/html/pubs/cbaivl98p.pdf

  24. Huang, P.W., Dai, S.K.: Image retrieval by texture similarity. Pattern Recogn. 36(3), 665–679 (2003)

    Article  Google Scholar 

  25. Lin, C.-H., Chen, R.-T., Chan, Y.-K.: A smart content-based image retrieval system based on color and texture feature. Image Vis. Comput. 27(6), 658–665 (2009)

    Article  Google Scholar 

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Acknowledgements

We would thank Xiangtan University with the construction of key disciplines in Hunan program. This research has been supported by NSFC (61672495), Scientific Research Fund of Hunan Provincial Education Department (16A208), and the Open Project Program of The State Key Lab of Digital Technology And Application of Settlement Cultural Heritage, Hengyang Normal University.

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Authors and Affiliations

  1. College of Information Engineering, Xiangtan University, Xiangtan, 411105, China

    Jianquan Ouyang, Hao He, Minnan Chu, Dong Chen & Huanrong Tang

  2. Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, College of Information Engineering, Xiangtan University, Xiangtan, 411105, China

    Jianquan Ouyang, Hao He, Minnan Chu, Dong Chen & Huanrong Tang

Authors
  1. Jianquan Ouyang
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  2. Hao He
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  3. Minnan Chu
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  4. Dong Chen
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  5. Huanrong Tang
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Corresponding author

Correspondence to Jianquan Ouyang .

Editor information

Editors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rui Mao

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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Ouyang, J., He, H., Chu, M., Chen, D., Tang, H. (2019). Android Oriented Image Visualization Exploratory Search. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_4

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  • DOI: https://doi.org/10.1007/978-981-15-0121-0_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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