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Content-Based Image Retrieval Using Statistical Color Occurrence Feature on Multiresolution Dataset

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Evolution in Computational Intelligence

Abstract

In modern life, the increasing use of different image-taking devices made image acquisition no longer a difficult task. To access a huge quantity of images having different resolutions stored in the dataset, the images must be kept in an organized manner. Content-Based Image Retrieval (CBIR) is an application of image retrieval problem, that is searching for a digital image from image dataset. Then term “content” in the context refers to some features that can be derived from the image itself. Color features are one of the important content of image which plays a vital role in image retrieval. Existing color features concentrate the only occurrence of pixel values or the correlation between pixel values. This paper proposed a new color feature which combines information about color shade percentage, color pixel occurrence percentage, pixel having maximum and minimum occurrence altogether. At the same time, proposed feature vector has a significantly reduced length which reduce computational cost of the retrieval system. This new feature is applied to one computer-generated image dataset (NITW-7500) and it’s translated and multiresolution version (using bilinear interpolation) and one standard natural image dataset (Corel-1K). Performance is improved in all variations of computer-generated images.

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References

  1. Ramos, J., Kockelkorn, T.T., Ramos, I., Ramos, R., Grutters, J., Viergever, M.A., van Ginneken, B., Campilho, A.: Content-based image retrieval by metric learning from radiology reports: application to interstitial lung diseases. IEEE J. Biomed. Health Inform. 20(1), 281–292 (2014)

    Article  Google Scholar 

  2. Chen, J.J., Su, C.R., Grimson, W.E.L., Liu, J.L., Shiue, D.H.: Object segmentation of database images by dual multiscale morphological reconstructions and retrieval applications. IEEE Trans. Image Process. 21(2), 828–843 (2011)

    Article  MathSciNet  Google Scholar 

  3. Rukundo, O., Maharaj, B.T.: Optimization of image interpolation based on nearest neighbour algorithm. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 641–647 (2014)

    Google Scholar 

  4. Gribbon, K.T., Bailey, D.G.: A novel approach to real-time bilinear interpolation. In: Proceedings. DELTA 2004. Second IEEE International Workshop on Electronic Design, Test and Applications, pp. 126–131 (2004)

    Google Scholar 

  5. Gao, S., Gruev, V.: Bilinear and bicubic interpolation methods for division of focal plane polarimeters. Opt. Express 19(27), 26161–26173 (2011)

    Article  Google Scholar 

  6. Chen, F., Wong, P.J.: On periodic discrete spline interpolation: quintic and biquintic cases. J. Comput. Appl. Math. 255, 282–296 (2014)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  8. Karami, E., Prasad, S., Shehata, M.: Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint arXiv:1710.02726 (2017)

  9. Julesz, B.: A theory of preattentive texture discrimination based on first-order statistics of textons. Biol. Cybern. 41(2), 131–138 (1981)

    Article  MathSciNet  Google Scholar 

  10. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. (6), 610–621 (1973)

    Google Scholar 

  11. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. (7), 971–987 (2002)

    Google Scholar 

  12. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with center-symmetric local binary patterns. In Computer Vision, Graphics and Image Processing, pp. 58–69. Springer, Berlin (2006)

    Google Scholar 

  13. Bhunia, A.K., Bhattacharyya, A., Banerjee, P., Roy, P.P., Murala, S.: A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern. Pattern Anal. Appl. 1–21 (2019)

    Google Scholar 

  14. Hu, R., Barnard, M., Collomosse, J.: Gradient field descriptor for sketch based retrieval and localization. In: 2010 IEEE International Conference on Image Processing, pp. 1025–1028. IEEE (2010)

    Google Scholar 

  15. Osowski, S.: Fourier and wavelet descriptors for shape recognition using neural networks—a comparative study. Pattern Recogn. 35(9), 1949–1957 (2002)

    Article  Google Scholar 

  16. Mathew, S.P., Balas, V.E., Zachariah, K.P.: A content-based image retrieval system based on convex hull geometry. Acta Polytech. Hung. 12(1), 103–116 (2015)

    Google Scholar 

  17. Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)

    Article  Google Scholar 

  18. Kanaparthi, S.K., Raju, U.S.N., Shanmukhi, P., Aneesha, G.K., Rahman, M.E.U.: Image retrieval by integrating global correlation of color and intensity histograms with local texture features. Multimedia Tools Appl. 1–37 (2019)

    Google Scholar 

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We have taken permission from competent authorities to use the images/data as given in the paper. In case of any dispute in the future, we shall be wholly responsible.

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Correspondence to Debanjan Pathak .

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Pathak, D., Raju, U.S.N., Singh, S., Naveen, G., Anil, K. (2021). Content-Based Image Retrieval Using Statistical Color Occurrence Feature on Multiresolution Dataset. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_66

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