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
Acharya, T., Ray, A. (2005). Image processing principles & applications. John Wiley & Sons, (Ch. 11)
Amanatiadis A, Kaburlasos V, Gasteratos A, Papadakis SE (2011) Evaluation of shape descriptors for shape based image retrieval. IET Image Process 5:493–499
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
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Han J, Ma K (2007) Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image Vis Comput 25(9):1474–1481
Haralick RM, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Tran Syst Man Cybern 3:610–621
Hiremath, H. S., Pujari, J., (2007). Content based image retrieval using colour, texture and shape features. International Conf. Adv. Comput. Commun., pp. 780–784
Huang, J., Kumar, R., Mitra, M., Zhu, W., (1997). Image indexing using colour correlograms. IEEE Conference Comput Vision Pattern Recognition 762–768
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
Jalab, A., (2011). Image retrieval system based on colour layout descriptor and Gabor filters. IEEE Conference Open System. (ICOS) pp. 32–36
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
Liu H, Yang Y (2013) Content-based image retrieval using colour difference histogram. Pattern Recogn 46:188–198
Liu GH, Li ZY, Zhang L, Xu Y (2011) Image retrieval based on micro-structure descriptor. Pattern Recogn 44(9):2123–2133
Mandal K, Aboulnasr T, Panchanathan S (1996) Image indexing using moments and wavelets. IEEE Trans Consum Electron 42(3):557–565
Manjunath BS, Ohm RJ (2001) Colour and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715
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
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29:51–59
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
Palm C (2004) Colour texture classification by integrative co-occurrence matrices. Pattern Recogn Lett 37:965–976
Park SB, Lee JW, Kim SK (2004) Content-based image classification using a neural network. Pattern Recogn Lett 25(3):287–300
Paschos G, Radev I, Prabakar N (2003) Image content-based retrieval using chromaticity moments. IEEE Trans Knowl Data Eng 15(5):1069–1072
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
Rahimi M, Moghaddam E (2013) A content based image retrieval system based on colour ton distributed descriptors. SIViP 9:691–704
Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE trans Syst Man Cybern 21(3):660–674
Shen LG, Wu J (2013) Content based image retrieval by combining colour texture and CENTRIST. IEEE int Workshop Signal Process 1:1–4
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
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
Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32
Vailaya A, Figueiredo MA, Jain AK, Zhang HJ (2001) Image classification for content-based indexing. IEEE Trans Image Process 10(1):117–130
Walia E, Pal A (2014) Fusion framework for effective color image retrieval. J Vis Commun Image Represent 25(6):1335–1348
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
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
Won CS, Park K, Park J (2002) Efficient use of MPEG-7 edge histogram descriptor. ETRI J 24(1):24–30
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
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
Zhang YJ (2008) Image classification and retrieval with mining technologies. Hand Res Text and Web Min Technol:96–110
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
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
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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