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Improved image retrieval using fast Colour-texture features with varying weighted similarity measure and random forests

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Abstract

Content-based image retrieval (CBIR) retrieves images from image database based on the visual similarity of query image. For the implementation of CBIR, feature extraction plays a significant role, where colour feature is quite remarkable. But, due to achromatic surfaces or unevenly colored, the role of texture is also important. This paper introduced an efficient and fast CBIR system, which is based on the combination of computationally light weighted colour and texture features viz. chromaticity moment, colour percentile, and local binary pattern. For searching, this paper proposes inverse variance based varying weighted similarity measure (low for high variance feature and high for low variance feature), which reduces the effect of redundancy by assigning the priority to each feature, and effectively retrieves relevant images. In addition, this paper also proposes query image classification and retrieval model by filtering out irrelevant class images using Random Forests (RF) classifier, which recovers the class of a query image based on distinct learning (supervised) of various decision trees. This successful ensemble classification of query images reduces the semantic gap, searching space, and enhances the retrieval performance. Extensive experimental analyses on benchmark databases confirm the usefulness and effectiveness of this work.

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References

  1. Acharya, T., Ray, A. (2005). Image processing principles & applications. John Wiley & Sons, (Ch. 11)

  2. Amanatiadis A, Kaburlasos V, Gasteratos A, Papadakis SE (2011) Evaluation of shape descriptors for shape based image retrieval. IET Image Process 5:493–499

    Article  Google Scholar 

  3. Bianconi F, Harvey R, Southam P, Andez A (2011) Theoretical and experimental comparison of different approaches for colour texture classification. School of Computing Sciences, University of East Anglia, UK, pp 1–20

    Google Scholar 

  4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  5. Han J, Ma K (2007) Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image Vis Comput 25(9):1474–1481

    Article  Google Scholar 

  6. Haralick RM, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Tran Syst Man Cybern 3:610–621

    Article  Google Scholar 

  7. Hiremath, H. S., Pujari, J., (2007). Content based image retrieval using colour, texture and shape features. International Conf. Adv. Comput. Commun., pp. 780–784

  8. Huang, J., Kumar, R., Mitra, M., Zhu, W., (1997). Image indexing using colour correlograms. IEEE Conference Comput Vision Pattern Recognition 762–768

  9. Irtaza A, Jaffar MA, Aleisa E, Choi TS (2014) Embedding neural networks for semantic association in content based image retrieval. Multimedia tools and appl 72(2):1911–1931

    Article  Google Scholar 

  10. Jalab, A., (2011). Image retrieval system based on colour layout descriptor and Gabor filters. IEEE Conference Open System. (ICOS) pp. 32–36

  11. Lin HC, Chen TR, Chan KY (2009) A smart content-based image retrieval system based on colour and texture feature. Image Vis Comput 27:658–665

    Article  Google Scholar 

  12. Liu H, Yang Y (2013) Content-based image retrieval using colour difference histogram. Pattern Recogn 46:188–198

    Article  Google Scholar 

  13. Liu GH, Li ZY, Zhang L, Xu Y (2011) Image retrieval based on micro-structure descriptor. Pattern Recogn 44(9):2123–2133

    Article  Google Scholar 

  14. Mandal K, Aboulnasr T, Panchanathan S (1996) Image indexing using moments and wavelets. IEEE Trans Consum Electron 42(3):557–565

    Article  Google Scholar 

  15. Manjunath BS, Ohm RJ (2001) Colour and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715

    Article  Google Scholar 

  16. Mistry, Y., Ingole, D. T., & Ingole, M. D. (2017). Content based image retrieval using hybrid features and various distance metric. Journal of Electrical Systems and Information Technology

  17. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29:51–59

    Article  Google Scholar 

  18. Oliva A, Torralba A (2001) Modeling the shape of the scene: A holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  19. Palm C (2004) Colour texture classification by integrative co-occurrence matrices. Pattern Recogn Lett 37:965–976

    Article  Google Scholar 

  20. Park SB, Lee JW, Kim SK (2004) Content-based image classification using a neural network. Pattern Recogn Lett 25(3):287–300

    Article  Google Scholar 

  21. Paschos G, Radev I, Prabakar N (2003) Image content-based retrieval using chromaticity moments. IEEE Trans Knowl Data Eng 15(5):1069–1072

    Article  Google Scholar 

  22. Raghupathi, G., Anand, S., & Dewal, L., (2010). Colour and texture features for content based image retrieval. Second International conference on multimedia and content based image retrieval 3, 39-57

  23. Rahimi M, Moghaddam E (2013) A content based image retrieval system based on colour ton distributed descriptors. SIViP 9:691–704

    Article  Google Scholar 

  24. Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE trans Syst Man Cybern 21(3):660–674

    Article  MathSciNet  Google Scholar 

  25. Shen LG, Wu J (2013) Content based image retrieval by combining colour texture and CENTRIST. IEEE int Workshop Signal Process 1:1–4

    Google Scholar 

  26. Singh, VP., Srivastava, R. (2015). Design & performance analysis of content based image retrieval system based on image classification using various feature sets, ABLAZE, Pages: 664–670

  27. Stricker, M., and Orengo, M. (1995). Similarity of colour images. Proc SPIE: Storage and Retrieval for Image and Video Databases III, vol. 2420, pp. 381-392

  28. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32

    Article  Google Scholar 

  29. Vailaya A, Figueiredo MA, Jain AK, Zhang HJ (2001) Image classification for content-based indexing. IEEE Trans Image Process 10(1):117–130

    Article  MATH  Google Scholar 

  30. Walia E, Pal A (2014) Fusion framework for effective color image retrieval. J Vis Commun Image Represent 25(6):1335–1348

    Article  Google Scholar 

  31. Wan, J., Wang, D., Hoi, S. C. H., Wu, P., Zhu, J., Zhang, Y., & Li, J. (2014). Deep learning for content-based image retrieval: A comprehensive study. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 157–166). ACM

  32. Wang J, Li J, Wiederhold G (2001) Simplicity: Semantics–sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963

    Article  Google Scholar 

  33. Won CS, Park K, Park J (2002) Efficient use of MPEG-7 edge histogram descriptor. ETRI J 24(1):24–30

    Article  MathSciNet  Google Scholar 

  34. Yildizer E, Balci AM, Hassan M, Alhajj R (2012) Efficient content-based image retrieval using multiple support vector machines ensemble. Expert Syst Appl 39(3):2385–2396

    Article  Google Scholar 

  35. Yue J, Li Z, Liu L, Fu Z (2011) Content-based image retrieval using colour and texture fused features. Math Comput Model 54:1121–1127

    Article  Google Scholar 

  36. Zhang YJ (2008) Image classification and retrieval with mining technologies. Hand Res Text and Web Min Technol:96–110

  37. Zhang M, Zhang K, Feng Q, Wang J, Lu Y (2014) ’A novel image retrieval method based on hybrid information descriptors’. J Vis Commun Image Represent 25(7):1574–1587

    Article  Google Scholar 

  38. Zhao Z, Tian Q, Sun H, Jin X, Guo J (2016) Content based image retrieval scheme using color, texture and shape features. Int J Signal Process, Image Process Pattern Recogn 9(1):203–212

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India

    Vibhav Prakash Singh & Rajeev Srivastava

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  1. Vibhav Prakash Singh
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  2. Rajeev Srivastava
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Correspondence to Vibhav Prakash Singh.

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Singh, V.P., Srivastava, R. Improved image retrieval using fast Colour-texture features with varying weighted similarity measure and random forests. Multimed Tools Appl 77, 14435–14460 (2018). https://doi.org/10.1007/s11042-017-5036-8

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  • DOI: https://doi.org/10.1007/s11042-017-5036-8

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