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Innovative local texture descriptor in joint of human-based color features for content-based image retrieval

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Abstract

Image retrieval is one of the hot research topics in computer vision which has been paid much attention by researchers in the last decade. Image retrieval refers to retrieving more similar images to the query form a huge image database. It is used widely in different scopes such as medical and search engines. Texture and color information play an important role in image content recognition. So, in this paper an innovative approach is proposed based on a combination of color and texture features. In this respect, an extended version of local neighborhood difference patterns (ELNDP) is proposed for the first time to achieve discriminative features. The ELNDP exploits the advantages of LBP and LNDP texture descriptors. Also, for global features extraction, optimized color histogram features in HSV color space are used to extract color features. Finally, the extended Canberra distance metric is used to retrieve more relevant images which is not sensitive to lower values like classic Canberra. The performance of the proposed approach is evaluated on five benchmark datasets such as Corel 1 K, 5 K, 10 K, STex and Colored Brodatz. The results are evaluated in terms of average precision rate (APR), average recall rate (ARR), The experimental results show that the proposed approach provides higher retrieval performance in comparison with state-of-the-art methods in this area such as machine learning-based and deep learning-based approaches.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Kanaparthi, S.K., Raju, U.S.N., Deep convolutional neural networks features for image retrieval, (2021).

  2. Agarwal, M., Singhal, A., Lall, B.: 3D local ternary co-occurrence patterns for natural, texture, face and bio medical image retrieval. Neurocomputing 313, 333–345 (2018). https://doi.org/10.1016/j.neucom.2018.06.027

    Article  Google Scholar 

  3. Agarwal, M., Singhal, A., Lall, B.: Multi-channel local ternary pattern for content-based image retrieval. Pattern Anal. Appl. 22, 1585–1596 (2019). https://doi.org/10.1007/s10044-019-00787-2

    Article  MathSciNet  Google Scholar 

  4. Armi, L., Fekri-Ershad, S., Texture image analysis and texture classification methods-A review, ArXiv Prepr. ArXiv1904.06554. (2019).

  5. Banerjee, P., Bhunia, A.K., Bhattacharyya, A., Roy, P.P., Murala, S.: Local Neighborhood Intensity Pattern–A new texture feature descriptor for image retrieval. Expert Syst. Appl. 113, 100–115 (2018). https://doi.org/10.1016/j.eswa.2018.06.044

    Article  Google Scholar 

  6. Chavda, S., Goyani, M.: Hybrid approach to content-based image retrieval using modified multi-scale LBP and color features. SN Comput. Sci. 1, 305 (2020). https://doi.org/10.1007/s42979-020-00321-w

    Article  Google Scholar 

  7. Corel 5k and Corel 10k Database, (n.d.). http: //www.ci.gxnu.edu.cn/cbir/Dataset.aspx (accessed Oct 26, 2020).

  8. Danapur, N., Dizaj, S.A.A., Rostami, V.: An efficient image retrieval based on an integration of HSV, RLBP, and CENTRIST features using ensemble classifier learning. Multimed. Tools Appl. 79, 24463–24486 (2020). https://doi.org/10.1007/s11042-020-09109-9

    Article  Google Scholar 

  9. Desai, P., Pujari, J., Sujatha, C., Kamble, A., Kambli, A.: Hybrid approach for content-based image retrieval using VGG16 layered architecture and SVM: an application of deep learning. SN Comput. Sci. 2, 170 (2021). https://doi.org/10.1007/s42979-021-00529-4

    Article  Google Scholar 

  10. Dey, M., Raman, B., Verma, M.: A novel colour- and texture-based image retrieval technique using multi-resolution local extrema peak valley pattern and RGB colour histogram. Pattern Anal. Appl. 19, 1159–1179 (2016). https://doi.org/10.1007/s10044-015-0522-y

    Article  MathSciNet  Google Scholar 

  11. Zhang, F., Zhong, B. (2017) Image Retrieval Based on Fused CNN Features, DEStech Trans. Comput. Sci. Eng. https://doi.org/10.12783/dtcse/aics2016/8171.

  12. Flexer, A., Schnitzer, D.: Choosing ℓp norms in high-dimensional spaces based on hub analysis. Neurocomputing 169, 281–287 (2015). https://doi.org/10.1016/j.neucom.2014.11.084

    Article  Google Scholar 

  13. Garg, M., Dhiman, G.: A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Comput. Appl. 33, 1311–1328 (2021). https://doi.org/10.1007/s00521-020-05017-z

    Article  Google Scholar 

  14. Gupta, S., Roy, P.P., Dogra, D.P., Kim, B.-G.: Retrieval of colour and texture images using local directional peak valley binary pattern. Pattern Anal. Appl. 23, 1569–1585 (2020). https://doi.org/10.1007/s10044-020-00879-4

    Article  Google Scholar 

  15. Huang, W., Wu, Q.: Image retrieval algorithm based on convolutional neural network. Curr. Trends Comput. Sci. Mech. Autom. (2017). https://doi.org/10.1515/9783110584974-034

    Article  Google Scholar 

  16. Irtaza, A., Jaffar, M.A., Aleisa, E., Choi, T.-S.: Embedding neural networks for semantic association in content based image retrieval. Multimed. Tools Appl. 72, 1911–1931 (2014). https://doi.org/10.1007/s11042-013-1489-6

    Article  Google Scholar 

  17. 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. Multimed. Tools Appl. (2019). https://doi.org/10.1007/s11042-019-08029-7

    Article  Google Scholar 

  18. Kayhan, N., Fekri-Ershad, S.: Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns. Multimed. Tools Appl. 80, 32763–32790 (2021). https://doi.org/10.1007/s11042-021-11217-z

    Article  Google Scholar 

  19. Liu, P., Guo, J.-M., Chamnongthai, K., Prasetyo, H.: Fusion of color histogram and LBP-based features for texture image retrieval and classification. Inf. Sci. (Ny) 390, 95–111 (2017). https://doi.org/10.1016/j.ins.2017.01.025

    Article  Google Scholar 

  20. Liu, P., Guo, J.-M., Wu, C.-Y., Cai, D.: Fusion of deep learning and compressed domain features for content-based image retrieval. IEEE Trans. Image Process. 26, 5706–5717 (2017). https://doi.org/10.1109/TIP.2017.2736343

    Article  MathSciNet  MATH  Google Scholar 

  21. Liu, H., Li, B., Lv, X., Huang, Y.: Image retrieval using fused deep convolutional features. Procedia Comput. Sci. 107, 749–754 (2017). https://doi.org/10.1016/j.procs.2017.03.159

    Article  Google Scholar 

  22. Liu, G.-H., Li, Z.-Y., Zhang, L., Xu, Y.: Image retrieval based on micro-structure descriptor. Pattern Recognit. 44, 2123–2133 (2011). https://doi.org/10.1016/j.patcog.2011.02.003

    Article  Google Scholar 

  23. Liu, G.-H., Yang, J.-Y.: Content-based image retrieval using color difference histogram. Pattern Recognit. 46, 188–198 (2013). https://doi.org/10.1016/j.patcog.2012.06.001

    Article  Google Scholar 

  24. Liu, G.-H., Zhang, L., Hou, Y.-K., Li, Z.-Y., Yang, J.-Y.: Image retrieval based on multi-texton histogram. Pattern Recognit. 43, 2380–2389 (2010). https://doi.org/10.1016/j.patcog.2010.02.012

    Article  MATH  Google Scholar 

  25. Long, F., Zhang, H., Feng, D.D., Fundamentals of content-based image retrieval BT multimedia information retrieval and management: technological fundamentals and applications, in: D.D. Feng, W.-C. Siu, H.-J. Zhang (Eds.), Springer Berlin Heidelberg, Berlin, Heidelberg, 2003: pp 1–26. https://doi.org/10.1007/978-3-662-05300-3_1.

  26. Garg, M., Malhotra, M., Singh, H., Comparison of deep learning techniques on content based image retrieval, Mod. Phys. Lett. A. (2019) 1950285. https://doi.org/10.1142/S0217732319502857.

  27. Murala, S., Maheshwari, R.P., Balasubramanian, R.: Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans. Image Process. 21, 2874–2886 (2012). https://doi.org/10.1109/TIP.2012.2188809

    Article  MathSciNet  MATH  Google Scholar 

  28. Niu, D., Zhao, X., Lin, X., Zhang, C.: A novel image retrieval method based on multi-features fusion. Signal Process. Image Commun. 87, 115911 (2020). https://doi.org/10.1016/j.image.2020.115911

    Article  Google Scholar 

  29. Nowaková, J., Prílepok, M., Snášel, V.: Medical image retrieval using vector quantization and fuzzy s-tree. J. Med. Syst. 41, 18 (2016). https://doi.org/10.1007/s10916-016-0659-2

    Article  Google Scholar 

  30. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623

    Article  MATH  Google Scholar 

  31. Roland Kwitt, P.M., Salzburg Texture Image Database, (n.d.). http://www.wavelab.at/sources/STex/ (accessed Oct 26, 2020).

  32. Salih, F.A.A., Abdulla, A.A.: Two-layer content-based image retrieval technique for improving effectiveness. Multimed. Tools Appl. (2023). https://doi.org/10.1007/s11042-023-14678-6

    Article  Google Scholar 

  33. Salih, S.F., Abdulla, A.A.: An effective bi-layer content-based image retrieval technique. J. Supercomput. 79, 2308–2331 (2023). https://doi.org/10.1007/s11227-022-04748-1

    Article  Google Scholar 

  34. Thusnavis Bella, M.I., Vasuki, A.: An efficient image retrieval framework using fused information feature. Comput. Electr. Eng. 75, 46–60 (2019). https://doi.org/10.1016/j.compeleceng.2019.01.022

    Article  Google Scholar 

  35. Verma, M., Raman, B.: Local tri-directional patterns: a new texture feature descriptor for image retrieval, Digit. Signal Process. 51, 62–72 (2016). https://doi.org/10.1016/j.dsp.2016.02.002

    Article  MathSciNet  Google Scholar 

  36. Verma, M., Raman, B.: Local neighborhood difference pattern: a new feature descriptor for natural and texture image retrieval. Multimed. Tools Appl. 77, 11843–11866 (2018). https://doi.org/10.1007/s11042-017-4834-3

    Article  Google Scholar 

  37. Verma, M., Raman, B., Murala, S.: Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing 165, 255–269 (2015). https://doi.org/10.1016/j.neucom.2015.03.015

    Article  Google Scholar 

  38. Viet Tran, L., Efficient image retrieval with statistical color descriptors, (2003).

  39. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23, 947–963 (2001). https://doi.org/10.1109/34.955109

    Article  Google Scholar 

  40. Wu, Q.: Image retrieval method based on deep learning semantic feature extraction and regularization softmax. Multimed. Tools Appl. 79, 9419–9433 (2020). https://doi.org/10.1007/s11042-019-7605-5

    Article  Google Scholar 

  41. Zhou, J., Liu, X., Liu, W., Gan, J.: Image retrieval based on effective feature extraction and diffusion process. Multimed. Tools Appl. 78, 6163–6190 (2019). https://doi.org/10.1007/s11042-018-6192-1

    Article  Google Scholar 

  42. Zhou, J., Liu, X., Xu, T., Gan, J., Liu, W.: A new fusion approach for content based image retrieval with color histogram and local directional pattern. Int. J. Mach. Learn. Cybern. 9, 677–689 (2018). https://doi.org/10.1007/s13042-016-0597-9

    Article  Google Scholar 

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Kelishadrokhi, M.K., Ghattaei, M. & Fekri-Ershad, S. Innovative local texture descriptor in joint of human-based color features for content-based image retrieval. SIViP 17, 4009–4017 (2023). https://doi.org/10.1007/s11760-023-02631-x

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