Abstract
Retrieving images from the large databases has always been one challenging problem in the area of image retrieval while maintaining the higher accuracy and lower computational time. Texture defines the roughness of a surface. For the last two decades due to the large extent of multimedia database, image retrieval has been a hot issue in image processing. Texture images are retrieved in a variety of ways. This paper presents a survey on various texture image retrieval methods. It provides a brief comparison of various texture image retrieval methods on the basis of retrieval accuracy and computation time with the benchmark databases. Image retrieval techniques vary with feature extraction methods and various distance measures. In this paper, we present a survey on various texture feature extraction methods by applying variants of wavelet transform. This survey paper facilitates the researchers with background of progress of image retrieval methods that will help researchers in the area to select the best method for texture image retrieval appropriate to their requirements.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Candès, E.J., Donoho, D.L.: New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities. Commun. Pure Appl. Math. 57(2), 219–266 (2004)
Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14(12), 2091–2106 (2005)
Velisavljevic, V., Beferull-Lozano, B., Vetterli, M., Dragotti, P.L.: Directionlets: anisotropic multi-directional representation with separable filtering. IEEE Trans. Image Process. 17(7), 1916–1933 (2006)
Reddy, A.H., Chandra. N.S.: Local oppugnant color space extrema patterns for content based natural and texture image retrieval. Int. J. Electron. Commun. (AEÜ) 69(1), 290–298 (2014)
MIT Vision and Modeling Group, Vision Texture. http://vismod.www.media.mit.edu
Pi, M.H., Tong, C.S., Choy, S.K., Zhang, H.: A fast and effective model for wavelet subband histograms and its application in texture image retrieval. IEEE Trans. Image Process. 15(10), 3078–3088 (2006)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1996)
Kokare, M., Biswas, P.K., Chatterji, B.N.: Rotation invariant texture image retrieval using rotated complex wavelet filters. IEEE Trans. Syst. Man Cybern. 36(6), 1273–1282 (2006)
Kokare, M., Biswas, P.K., Chatterji, B.N.: Texture image retrieval using new rotated complex wavelet filters. IEEE Trans. Syst. Man, Cybern. 35(6), 1168–1178 (2005)
Randen, T., Husoy, J.H.: Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 291–310 (1999)
Wouwer, G.V., Scheunders, P., Dyck, D.V.: Stastical texture characterization from discrete wavelet representation. IEEE Trans. Image Process. 8(4), 592–598 (1999)
Kingsbury, N.G.: Image processing with complex wavelet. Phil. Trans. Roy. Soc. London A 357, 2543–2560 (1999)
Zujovic, J., Pappas, T.N., Neuhoff, D.L.: Structural texture similarity metrics for image analysis and retrieval. IEEE Trans. Image Process. 22(7), 2545–2558 (2013)
Corbis Stock Photography. http://www.corbis.com (2011). Accessed 23 March 2011
CUReT: Columbia-Utrecht Refelctance and Texture Database. http://www1.cs.columbia.edu/CAVE/software/curet (2002). Accessed 4 Aug 2002
Dong, Y., Tao, D., Li, X., Ma, J., Pu, J.: Texture classification and retrieval using shearlets and linear regression. IEEE Trans. Cybern. 45(3), 358–369 (2015)
Shrivastava, N., Tyagi, V.: A review of ROI image retrieval techniques. Adv. Intell. Syst. Comput. 328, 509–520 (2015). doi:10.1007/978-3-319-12012-6_56
Jeena Jacob, I., Srinivasagan, K.G., Jayapriya, K.: Local oppugnant color texture pattern for image retrieval system. Pattern Recogn. Lett. 42, 72–78 (2014)
Mukhopadhyay, S., Dash, J.K., Das Gupta, R.: Content-based texture image retrieval using fuzzy class membership. Pattern Recogn. Lett. 34(6), 646–654 (2013)
Verma, M., Raman, B., Murala, S.: Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing (2015). doi:10.1016/j.neucom.2015.03.015
Kwitt, R., Uhl, A.: Lightweight probabilistic texture retrieval. IEEE Trans. Image Process. 19(1), 241–253 (2010)
Choy, S.K., Tong, C.S.: Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval. IEEE Trans. Image Process. 19(2), 281–289 (2010)
Pi, M., Li, H.: Fractal indexing with the joint statistical properties and its application in texture. IET Image Process 2(4), 218–230 (2008)
Do, M.N., Vetterli, M.: Wavelet-based texture retrieval using generalized gaussian density and Kullback–Leibler distance. IEEE Trans. Image Process. 11(2), 146–158 (2002)
Nava, R., Escalante-RamÃrez, B., Cristóbal, G.: Texture image retrieval based on log-gabor features. Progress Pattern Recogn. Image Anal. Comput. Vis. Appl. 7441, 414–421 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Raghuwanshi, G., Tyagi, V. (2016). A Survey on Texture Image Retrieval. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 381. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2526-3_44
Download citation
DOI: https://doi.org/10.1007/978-81-322-2526-3_44
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2525-6
Online ISBN: 978-81-322-2526-3
eBook Packages: EngineeringEngineering (R0)