New Texture Features Based on Wavelet Transform Coinciding with Human Visual Perception

Wavelet decomposition is widely used in texture image retrieval and classification. Subbands statistics are normally used as feature vectors during feature selection phase. However, most previous methods seldom make further analysis of the transforms' subbands according to human visual perception. Based on wavelet decomposition, this paper proposes three new texture features which coincide with human visual perception. These features include directionality, contrast and coarseness in wavelet domain. We test our new texture features using the Brodatz texture database, and the experimental results indicate that the new texture features are consistent with human visual perception well.

[1]  Fuhui Long,et al.  Fundamentals of Content-Based Image Retrieval , 2003 .

[2]  Shih-Fu Chang,et al.  Quad-tree segmentation for texture-based image query , 1994, MULTIMEDIA '94.

[3]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[6]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

[7]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Sebastiano Battiato,et al.  Perceptive visual texture classification and retrieval , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[9]  Minh N. Do,et al.  Texture similarity measurement using Kullback-Leibler distance on wavelet subbands , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[10]  B. S. Manjunath,et al.  A texture descriptor for browsing and similarity retrieval , 2000, Signal Process. Image Commun..

[11]  Shih-Fu Chang,et al.  Transform features for texture classification and discrimination in large image databases , 1994, Proceedings of 1st International Conference on Image Processing.

[12]  B. S. Manjunath,et al.  A comparison of wavelet transform features for texture image annotation , 1995, Proceedings., International Conference on Image Processing.

[13]  Shawn Newsam,et al.  A texture descriptor for image retrieval and browsing , 1999, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL'99).