Abstract
The emergence of low cost digital cameras and other image capturing devices has created a huge amount of different types of images. Accessing images easily requires proper arrangement and indexing of images. This has made image retrieval an important problem of Computer Vision. This paper attempts to decompose a Local Binary Pattern (LBP) image at multiple resolution to extract structural arrangement of pixels more efficiently than processing a single scale of the LBP image. LBP descriptors of the 2-D gray scale image are computed followed by computation of Discrete Wavelet Transform (DWT) coefficients of the resulting 2-D LBP image. Finally, construction of feature vector is done through Gray-Level Co-occurrence Matrix. Performance of the proposed method is tested on two benchmark datasets, Corel-1K and Corel-5K, and measured in terms of Precision and Recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods, which proves the effectiveness of the proposed method.
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References
Dutta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 5:1–5:60 (2008)
Smith, J.R., Chang, S.F.: Tools and techniques for color image retrieval. Electron. Imaging Sci. Technol. Int. Soc. Opt. Photonics 2670, 426–437 (1996)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)
Srivastava, P., Binh, N.T., Khare, A.: Content-based image retrieval using moments. In: Vinh, P.C., Alagar, V., Vassev, E., Khare, A. (eds.) ICCASA 2013. LNICST, vol. 128, pp. 228–237. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05939-6_23
Srivastava, P., Binh, N.T., Khare, A.: Content-based image retrieval using moments of local ternary pattern. Mob. Netw. Appl. 19, 618–625 (2014)
Srivastava, P., Prakash, O., Khare, A.: Content-based image retrieval using moments of wavelet transform. In: International Conference on Control Automation and Information Sciences, Gwangju, South Korea, pp. 159–164 (2014)
Youssef, S.M.: ICTEDCT-CBIR: integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Comput. Electr. Eng. 38, 1358–1376 (2012)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray scale rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Liu, G., Zhang, L., Hou, Y., Yang, J.: Image retrieval based on multi-texton histogram. Pattern Recogn. 43(7), 2380–2389 (2008)
Liu, G., Li, Z., Zhang, L., Xu, Y.: Image retrieval based on microstructure descriptor. Pattern Recogn. 44(9), 2123–2133 (2011)
Liu, G.H., Yang, J.Y.: Content-based image retrieval using color difference histogram. Pattern Recogn. 46(1), 188–198 (2013)
Zhang, M., Zhang, K., Feng, Q., Wang, J., Kong, J., Lu, Y.: A novel image retrieval method based on hybrid information descriptors. J. Vis. Commun. Image Represent. 25(7), 1574–1587 (2014)
Feng, L., Wu, J., Liu, S., Zhang, H.: Global correlation descriptor: a novel image representation for image retrieval. J. Vis. Commun. Image Represent. 33, 104–114 (2015)
Xia, Yu., Wan, S., Jin, P., Yue, L.: Multi-scale local spatial binary patterns for content-based image retrieval. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds.) AMT 2013. LNCS, vol. 8210, pp. 423–432. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02750-0_45
Zhang, D., Islam, M.M., Lu, G., Sumana, I.J.: Rotation invariant curvelet features for region based image retrieval. Int. J. Comput. Vis. 98(2), 187–201 (2012)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall Press, Englewood Cliffs (2002)
Haralick, R.M., Shanmungam, K., Dinstein, I.: Textural features of image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)
http://wang.ist.psu.edu/docs/related/. Accessed Oct 2017
http://www.ci.gxnu.edu.cn/cbir/. Accessed Oct 2017
Acknowledgements
This work was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2016M3C7A1905477), and the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2017R1D1A1B03036423).
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Khare, M., Srivastava, P., Gwak, J., Khare, A. (2018). A Multiresolution Approach for Content-Based Image Retrieval Using Wavelet Transform of Local Binary Pattern. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_50
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