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Content-Based Image Retrieval Based on Shape Similarity Calculation

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3D Research

Abstract

In the content-based image retrieval technology, the performance of retrieval system using only a single image feature is generally unsatisfactory, and therefore the image retrieval system using two or more image features is more often used. When there is the target deformation or the size variation, the performance of image retrieval system using only shape features is not satisfactory, too. To solve these problems, in this paper, the extraction of image salient region and a shape representation methods of describing the image content are proposed, then they are used with image texture and color features to implement image retrieval. Experimental results show that the proposed image retrieval system can provide very good retrieval performance.

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References

  1. Pandey, S., & Khanna, P. (2016). Content-based image retrieval embedded with agglomerative clustering built on information loss. Computers & Electrical Engineering, 54, 506–521.

    Article  Google Scholar 

  2. Elalami, M. E. (2011). A novel image retrieval model based on the most relevant features. Knowledge-Based Systems, 24, 23–32.

    Article  Google Scholar 

  3. Jin, C., & Jin, S. W. (2015). Automatic image annotation using feature selection based on improving quantum particle swarm optimization. Signal Processing, 109, 172–181.

    Article  Google Scholar 

  4. Liu, W. F., Tao, D. C., Cheng, J., & Tang, Y. Y. (2014). Multiview Hessian discriminative sparse coding for image annotation. Computer Vision and Image Understanding, 118, 50–60.

    Article  Google Scholar 

  5. Shrivastava, N., & Tyagi, V. (2014). Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching. Information Sciences, 259, 212–224.

    Article  Google Scholar 

  6. Wang, X. Y., Wu, J. F., & Yang, H. Y. (2010). Robust image retrieval based on color histogram of local feature regions. Multimedia Tools Application, 49, 323–345.

    Article  Google Scholar 

  7. Jin, C., & Jin, S. W. (2016). Image distance metric learning based on neighborhood sets for automatic image annotation. Journal of Visual Communication and Image Representation, 34, 167–175.

    Article  Google Scholar 

  8. Min, H., Shu, H., Ma, Y., Huang, M., Shu, H., Ma, Y., et al. (2015). Content-based image retrieval technology using multi-feature fusion. Optik-International Journal for Light and Electron Optics, 126, 2144–2148.

    Article  Google Scholar 

  9. Murala, S., & Wu, Q. M. J. (2014). Expert content-based image retrieval system using robust local patterns. Journal of Visual Communication and Image Representation, 25, 1324–1334.

    Article  Google Scholar 

  10. Yang, H., & Zhou, X. (2010). Research of content based image retrieval technology. In The third international symposium on electronic commerce and security workshops (ISECS2010), China (pp. 29–31).

  11. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2015). Information fusion between short term learning and long term learning in content based image retrieval systems. Multimedia Tools and Applications, 74, 3799–3822.

    Article  Google Scholar 

  12. Huang, P. W., & Dai, S. K. (2003). Image retrieval by texture similarity. Pattern Recognition, 36, 665–679.

    Article  Google Scholar 

  13. Chaudhary, M. D., Upadhyay, A. B. (2014). Fusion of local and global features using stationary wavelet transform for efficient content based image retrieval. In IEEE students’ conference on electrical, electronics and computer science (SCEECS) (pp. 1–6).

  14. Lin, C. H., Chen, R. T., & Chan, Y. K. (2009). A smart content-based image retrieval system based on color and texture feature. Image and Vision Computing, 27, 658–665.

    Article  Google Scholar 

  15. Jhanwar, N., Chaudhuri, S., Seetharaman, G., & Zavidovique, B. (2004). Content based image retrieval using motif co-occurrence matrix. Image and Vision Computing, 22, 1211–1220.

    Article  Google Scholar 

  16. Jain, A. K., & Vailaya, A. (1996). Image retrieval using color and shape. Pattern Recognition, 29, 1233–1244.

    Article  Google Scholar 

  17. Yang, J. Y., Wang, H. X., Yuan, J. S., Li, Y. F., & Liu, J. Y. (2016). Invariant multi-scale descriptor for shape representation, matching and retrieval. Computer Vision and Image Understanding, 145, 43–58.

    Article  Google Scholar 

  18. Chaudharil, R., & Patil, A. M. (2012). Content based image retrieval using color and shape features. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 1, 386–392.

    Google Scholar 

  19. Han, J., & Ma, K. K. (2002). Fuzzy color histogram and its use in color image retrieval. IEEE Transactions on Image Processing, 11, 944–952.

    Article  Google Scholar 

  20. Wenfei, D., Shuchun, Y., Songyu, L., Zhiqiang, Z., & Wenbo, G. (2014). Image retrieval based on multi-feature fusion. In Fourth international conference on instrumentation and measurement, computer, communication and control (pp. 240–243).

  21. Wang, X. Y., Zhang, B. B., & Yang, H. Y. (2014). Content-based image retrieval by integrating color and texture features. Multimedia Tools and Applications, 68, 545–569.

    Article  Google Scholar 

  22. Nandagopalan, S., Adiga, D. B. S., & Deepak, N. (2009). A universal model for content based image retrieval. International Journal of Computer Science, 4, 531–538.

    Google Scholar 

  23. Kekre, D. H. B., Sudeep, D. T., & Akshay, M. (2010). Image retrieval using fractional coefficients of transformed image using DCT and Walsh transform. International Journal of Engineering Science and Technology, 2, 362–371.

    Google Scholar 

  24. Ghosh, S., & Dubey, S. K. (2013). Comparative analysis of k-means and fuzzy c-means algorithms. International Journal of Advanced Computer Science and Applications, 4, 34–39.

    Google Scholar 

  25. Fareed, M. M. S., Ahmed, G., & Chun, Q. (2015). Salient region detection through sparse reconstruction and graph-based ranking. Journal of Visual Communication and Image Representation, 32, 144–155.

    Article  Google Scholar 

  26. Zhao, Q., & Koch, C. (2013). Learning saliency-based visual attention: A review. Signal Processing, 93, 1401–1407.

    Article  Google Scholar 

  27. Jia, C., Qi, J., Li, X., & Lu, H. (2016). Saliency detection via a unified generative and discriminative model. Neurocomputing, 173, 406–417.

    Article  Google Scholar 

  28. Suthaharan, S. (2016). Support vector machine. Machine learning models and algorithms for big data classification (pp. 207–235). New York: Springer.

    Google Scholar 

  29. http://savvash.blogspot.com/2008/12/benchmark-databases-for-cbir.html.

  30. http://www.robots.ox.ac.uk/~vgg/data/flowers/17/.

  31. http://www.vision.caltech.edu/Image_Datasets/Caltech256/.

  32. Zhang, M., Zhang, K., Feng, Q., Wang, J., Kong, J., & Lu, Y. (2014). A novel image retrieval method based on hybrid information descriptors. Journal of Visual Communication and Image Representation, 25, 1574–1587.

    Article  Google Scholar 

  33. Das, G., Ray, S., & Wilson, C. (20016). Feature re-weighting in content-based image retrieval. In International conference on image and video retrieval. Berlin: Springer (pp. 193–200).

  34. Liu, G. H., Li, Z. Y., Zhang, L., & Xu, Y. (2011). Image retrieval based on micro-structure descriptor. Pattern Recognition, 44, 2123–2133.

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Social Science Foundation of China (Grant No. 13BTQ050).

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Authors and Affiliations

  1. School of Computer, Central China Normal University, Wuhan, 430079, Hubei, China

    Cong Jin & Shan-Wu Ke

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  1. Cong Jin
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  2. Shan-Wu Ke
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Correspondence to Cong Jin.

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Jin, C., Ke, SW. Content-Based Image Retrieval Based on Shape Similarity Calculation. 3D Res 8, 23 (2017). https://doi.org/10.1007/s13319-017-0132-0

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  • DOI: https://doi.org/10.1007/s13319-017-0132-0

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