Classifying image texture with statistical landscape features

Pattern Analysis and Applications - Tập 8 - Trang 321-331 - 2005
Cun Lu Xu1, Yan Qiu Chen1
1Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai, China

Tóm tắt

This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.

Tài liệu tham khảo

Coggins JM (1982) A framework for texture analysis based on spatial filtering. PhD thesis, Computer Science Department, Michigan State University Tamura H, Mori S, Yamawaki Y (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern SMC 8(6):460–473 Haralick RM (1979). Statistical and structural approaches to texture. Proc IEEE 67(5):786–804 Karu K, Jain AK, Bolle RM (1996) Is there any texture in the image? Pattern Recognit 29(9):1437–1446 Randen T, Husøy JH (1999) Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal Mach Intell 21(4):291–310 Panjwani DK, Healey G (1995) Markov random field models for unsupervised segmentation of textured color images. IEEE Trans Pattern Anal Mach Intell 17(10):939–954 Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842 Clerc M, Mallat S (2002) The texture gradient equation for recovering shape from texture. IEEE Trans Pattern Anal Mach Intell 24(4):536–549 Singh M, Singh S (2002) Spatial texture analysis: a comparative study. In: Proceedings of the 15th international conference on pattern recognition (ICPR’02), vol 1, pp 676–679 Tuceryan M, Jain AK (1993) Texture analysis. Handbook pattern recognition and computer vision, chap 2. In: Chen CH, Pau LF, Wang PSP (eds) World Scientific, Singapore, pp 235–276 Weszka JS, Dyer CR, Rosenfeld A (1976) A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cybern SMC 6(4):269–285 Jones DJ, Jackway PT (2000) Granolds: a novel texture representation. Pattern Recognit 33(6):1033–1045 Sivakumar K, Goutsias J (1999) Morphologically constrained GRFS: applications to texture synthesis and analysis. IEEE Trans Pattern Anal Mach Intell 21(2):99–113 Rosenfeld A, Thurston M (1971) Edge and curve detection for visual scene analysis. IEEE Trans Comput C-20:562–569 Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC 3(6):610–621 Conners RW, Harlow CA (1980) A theoretical comparison of texture algorithms. IEEE Trans Pattern Anal Mach Intell 2(3):204–222 Murino V, Ottonello C, Pagnan S (1998) Noisy texture classification: a higher order statistics approach. Pattern Recognit 34(4):383–393 Liu XW, Wang DL (2003) Texture classification using spectral histograms. IEEE Trans Image Process 12(6):661–670 Kaplan LM (1999) Extended fractal analysis for texture classification and segmentation. IEEE Trans Image Process 8(11):1572–1585 Krishnamachari S, Chellappa R (1997) Multiresolution gauss-markov random field models for texture segmentation. IEEE Trans Image Process 6(2):251–267 Cross G, Jain A (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell 5(1):25–39 Bennett J, Khotanzad A (1998) Modeling textured image using generalized long correlation models. IEEE Trans Pattern Anal Mach Intell 20(12):1365–1370 Garcia P, Petrou M, Kamata S (1999) The use of Boolean model for texture analysis of grey images. Comput Vis Image Underst 74(3):227–235 Laws KI (1980) Rapid texture identification. In: Proceedings of the SPIE conference image processing for missile guidance, pp 376–380 Jain AK, Farrokhnia F (1991) Unsupervised texture segmentation using Gabor filters. Pattern Recognit 24(12):1167–1186 Azencott R, Wang JP, Younes L (1997) Texture classification using windowed fourier filters. IEEE Trans Pattern Anal Mach Intell 19(2):148–153 Arivazhagan S, Ganesan L (2003) Texture classification using wavelet transform. Pattern Recognit Lett 24(9–10):1513–1521 Pun C-M, Lee M-C (2003) Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans Pattern Anal Mach Intell 25(5):590–603 Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4:1549–1560 Mojsilovic A, Popovic MV, Rackov DM (2000) On the selection of an optimal wavelet basis for texture classification. IEEE Trans Image Process 9(12):2043–2050 Chen YQ, Nixon MS, Thomas DW (1995) Statistical geometrical features for texture classification. Pattern Recognit 28(4):537–552 Brodatz P (1966) Textures: a photographic album for artists and designers. Dover, Paris. http://www.ux.his.no/∼tranden/brodatz.html Picard R (1995) Chris Graczyk, Steve Mann, Josh Wachman, Len Picard, and Lee Campbell. Vistex. via http://ftp:whitechapel.media.mit.edu. Copyright 1995 Massachusetts Institute of Technology Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice Hall International, Eagle Cliffs Fukunaga K, Hostetler LD (1973). Optimization of k-nearest-neighbor density estimates. IEEE Trans Inform Theory IT-19:320–326