Analysis of irregularly shaped texture regions: a comparative study

Most of texture classification techniques are evaluated using large rectangular samples of each texture. This, however, is unrealistic, especially as samples of a certain texture may be used as cues in searching image databases or identifying objects in a scene. In this paper, four different texture classification methods (wavelets, co-occurrence matrices, sum and difference histograms, and 1D Boolean models) are systematically compared and evaluated with respect to their performance in identifying textures from small and irregular samples.

[1]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[2]  Maria Petrou,et al.  Locating boundaries of textured regions , 1997, IEEE Trans. Geosci. Remote. Sens..

[3]  Maria Petrou,et al.  The Use of Boolean Model for Texture Analysis of Grey Images , 1999, Comput. Vis. Image Underst..

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Richard C. Dubes,et al.  Performance evaluation for four classes of textural features , 1992, Pattern Recognit..

[8]  Michael Unser,et al.  Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Maria Petrou,et al.  Classification of binary textures using the 1-D Boolean model , 1999, IEEE Trans. Image Process..