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.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-018-5283-7/MediaObjects/11277_2018_5283_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-018-5283-7/MediaObjects/11277_2018_5283_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-018-5283-7/MediaObjects/11277_2018_5283_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-018-5283-7/MediaObjects/11277_2018_5283_Fig4_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-018-5283-7/MediaObjects/11277_2018_5283_Fig5_HTML.gif)
Similar content being viewed by others
References
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.
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.
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.
Liu, S., Fu, W., & Deng, H. (2013). Distributional fractal creating algorithm in parallel environment. International Journal of Distributed Sensor Networks, 9(9), 1–10.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Liu, S., Fu, W., & He, L. (2017). Distribution of primary additional errors in fractal encoding method. Multimedia Tools and Applications, 76(4), 5787–5802.
Acknowledgements
We thank the anonymous reviewers for the valuable feedback on earlier versions of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, W. A Novel Web Image Retrieval Method. Wireless Pers Commun 103, 1153–1160 (2018). https://doi.org/10.1007/s11277-018-5283-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-018-5283-7