Abstract
Texture retrieval is a vital branch of content-based image retrieval. Rotation-invariant texture retrieval plays a key role in texture retrieval. This paper addresses three major issues in rotation-invariant texture retrieval: how to select the texture measurement methods, how to alleviate the influence of rotation for texture retrieval and how to apply the proper multi-scale analysis theory for texture images. First, the spectrum influence between a Radon transform and a Log-polar transform was compared after the rotation effect was eliminated. The average retrieval performance of wavelet and NSCT with different retrieval parameters was also evaluated. Based on such analysis, we developed a multi-scale and multi-orientation texture transform spectrum, as well as a rotation-invariant feature vector and its measurement criteria, which can represent the human visual perception sensitive to texture energy. Then a new rotation-invariant texture retrieval algorithm was proposed. The algorithm was developed based on non-parametric statistical features and it applies low band and high frequency directional bands of NSCT coefficients for coarse filtering and fine retrieval, respectively. Experiments on the Brodatz image database show that the constructed rotation-invariant feature vector is appropriate for capturing major orientations and describing detail information. Besides, the combination of the two-step progressive retrieval strategy and multi-scale analysis method can effectively improve retrieval efficiency while ensuring a high precision compared with traditional algorithms.
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References
Kourosh J K, Hamid S Z. Rotation-invariant multiresolution texture analysis using radon and wavelet transforms. IEEE Trans Image Process, 2005, 14: 783–795
Chen Y W, Chen Y Q. Invariant description and retrieval of planar shapes using Radon composite features. IEEE Trans Signal Process, 2008, 56: 4762–4771
Pun C M, Lee M C. Log-Polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans Pattern Analysis Mach Intell, 2003, 25: 590–603
Campbell F W, Robson J G. Application of Fourier analysis to the visibility of gratings. J Physiol (Lond), 1968, 197: 551–566
Liu Y, Zhang D S, Lu G J, et al. A survey of content-based image retrieval with high-level semantics. Pattern Recogn, 2007, 40: 262–282
Do M N, Vetterli M. Wavelet Based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans Image Process, 2002, 11: 146–158
Ma WY, Manjunath B. Netra: a toolbox for navigating large image databases. In: Proceedings of the IEEE International Conference on Image Processing, 1997. 568–571
Po D D Y, Do M N. Directional multiscale modeling of images using the contourlet transform. IEEE Trans Image Process, 2006, 15: 1610–1620
Randen T, Husoy J H. Filtering for texture classification: a comparative study. IEEE Trans Pattern Recogn Mach Intell, 1999, 21: 291–310
Montoya-Zegarra J A, Papa J P, Leite N J, et al. Learning how to extract rotation-invariant and scale-invariant features from texture images. Eurasip J Adv Signal Process, 2008, doi: 10.1155/2008/691924
Kourosh J K, Hamid S Z. Radon transform orientation estimation for rotation invariant texture analysis. IEEE Trans Patt Analys Mach, 2005, 27: 1004–1008
Franci L, Stanislav K. Rotation-invariant texture classification. Pattern Recogn Lett, 2003, 24: 1151–1161
Da Cunha A L, Zhou J P, Do M N. The nonsubsampled contourlet transform:theory, design, and applications. IEEE Trans Image Process, 2006, 15: 3089–3101
Wouwer G V, Scheunders P, Dyck D V. Statistical texture characterization from discrete wavelet representations. IEEE Trans Image Process, 1999, 8: 592–598
Heeger D J, Bergen J R. Pyramid-based texture analysis/synthesis. In: SIGGRAPH, Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, 1995. 229–238
Wu Y N, Zhu S C, Liu X. Equivalence of Julesz ensembles and FRAMEmodels. Int J Comput Vis, 2000, 38: 245–261
Zhu S C, Wu Y N, Mumford D. Minimax entropy principle and its application to texture modeling. Neur Compute, 1997, 9: 1627–1660
Lin L, Zhang Y, Sun T, et al. Contourlet Transform-Image Process and Application. Beijing: Science Press, 2008
Liu X Y, Wang D L. Texture classification using spectral histograms. IEEE Trans Image Process, 2003, 12: 661–670
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Shao, Z., Li, D. & Zhu, X. A multi-scale and multi-orientation image retrieval method based on rotation-invariant texture features. Sci. China Inf. Sci. 54, 732–744 (2011). https://doi.org/10.1007/s11432-011-4207-x
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DOI: https://doi.org/10.1007/s11432-011-4207-x