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A Novel Web Image Retrieval Method

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Abstract

Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy.

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References

  1. Binderberger, M. O., Mehrotra, S., Chakrabarti, K., & Porkaew, K. (2000). WEBMARS: A multimedia search engine. In Proceedings of the SPIE electronic imaging 2000: Internet imaging (Vol. 2000, pp. 23–38), San Jose, CA, USA.

  2. Berry, M. W., Wang, P., & Yang, Y. (2003). Mining longitudinal web queries: Trends and patterns. Journal of the American Society for Information Science and Technology, 54(8), 43–758.

    Google Scholar 

  3. Zheng, Z. G., Jeong, H. Y., Huang, T., & Shu, J. B. (2017). KDE based outlier detection on distributed data streams in multimedia network. Multimedia Tools and Applications, 76(17), 18027–18045.

    Article  Google Scholar 

  4. Liu, S., Fu, W., & Deng, H. (2013). Distributional fractal creating algorithm in parallel environment. International Journal of Distributed Sensor Networks, 9(9), 1–10.

    Google Scholar 

  5. Liu, S., Zhang, Z., & Qi, L. (2016). A fractal image encoding method based on statistical loss used in agricultural image compression. Multimedia Tools and Applications, 75(23), 853–856.

    Google Scholar 

  6. Zheng, Z. G., Wang, P., & Liu, J. (2015). Real-time big data processing framework: Challenges and solutions. Applied Mathematics & Information Sciences, 9(3), 2217–2237.

    MathSciNet  Google Scholar 

  7. Kherfi, M. L., Ziou, D., & Bernardi, A. (2008). Image retrieval from the World Wide Web: Issues, techniques, and system. ACM Computing Surveys, 36(1), 35–67.

    Article  Google Scholar 

  8. Zheng, L., Bo, Y., & Zhang, N. (2009). An improved link selection algorithm for vertical search engine. In Proceedings of the 1st international conference on information science and engineering (pp. 778–781), Nan**g, China. IEEE.

  9. Zheng, R., Wen, S. L., Zhang, Q., **, H., & **e, X. (2011). Compound face image retrieval based on vertical web image retrieval. In Proceedings of the sixth annual China grid conference (pp. 130–135), Liaoning, China. IEEE.

  10. Negrel, R., Picard, D., & Gosselin, P. H. (2013). Web-scale image retrieval using compact tensor aggregation of visual descriptors. IEEE Multimedia, 20(3), 24–33.

    Article  Google Scholar 

  11. Miguelena, B. A. M., Rivera, G. J. H., & Hernandez, A. M. (2014). Garabato: A proposal of a sketch-based image retrieval system for the web. In Proceedings of the 2014 international conference on electronics, communications and computers (CONIELECOMP) (pp. 26–28), Cholula, Mexico. IEEE.

  12. Bhagat, A., & Atique, M. (2014). Web based medical image retrieval system using fuzzy connectedness image segmentation and geometric moments. In Proceedings of the 2014 international conference on computational science and computational intelligence (CSCI) (pp. 208–214), Las Vegas, NV, USA. IEEE.

  13. Wan, Y., & Tong, H. (2008). URL assignment algorithm of crawler in distributed method based on hash. In Proceedings of the IEEE international conference on networking, sensing and control (pp. 1632–1635), Sanya, China. IEEE.

  14. Guerriero, A., Ragni, F., & Martines, C. (2010) A dynamic URL assignment method for parallel web crawler. In Proceedings of the 2010 IEEE international conference on computational intelligence for measurement systems and applications (CIMSA) (pp. 119–123), Taranto, Italy. IEEE.

  15. Meng, F. J., & Guo, B. L. (2011). Image retrieval by using local distribution features of interest points and multiple-instance learning. Journal of **dian University, 32(2), 256–259.

    Google Scholar 

  16. Hoangc, N. D., Thuong, L. T., Tuan, D. H., Cao, B. T., & Ty, N. X. (2014). Image retrieval using contourlet based interest points. In Proceedings of the 10th international conference on information science, signal processing and their applications (ISSPA 2010) (pp. 93–96), Kuala Lumpur, Malaysia. IEEE.

  17. Fu, X., & Zeng, J. X. (2014). A novel image retrieval method based on interest points matching and distribution. Chinese Journal of Laser, 37(3), 774–778.

    Google Scholar 

  18. Liu, S., Fu, W., & Zhao, W. (2013). A novel fusion method by static and moving facial capture. Mathematical Problems in Engineering. https://doi.org/10.1155/2013/503924.

    Article  Google Scholar 

  19. Liu, S., Fu, W., & He, L. (2017). Distribution of primary additional errors in fractal encoding method. Multimedia Tools and Applications, 76(4), 5787–5802.

    Article  Google Scholar 

Download references

Acknowledgements

We thank the anonymous reviewers for the valuable feedback on earlier versions of this paper.

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Correspondence to Wen** Li.

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Li, W. A Novel Web Image Retrieval Method. Wireless Pers Commun 103, 1153–1160 (2018). https://doi.org/10.1007/s11277-018-5283-7

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