Wavelet Domain Features for Texture/Pattern Description, Classification and Replicability Analysis
We present a new wavelet domain technique for texture/pattern analysis and test of replicability. The main property of the proposed features is that they measure pattern quality along the most important perceptual dimensions. In other words, we quantify and classify patterns according to their directionality, symmetry, regularity and type of regularity. After the feature extraction, pattern classification (i.e. replicability analysis) is performed by traversing a tree. The algorithm is tested on a database with 340 images demonstrating an excellent classification accuracy. Additionally, we demonstrate the efficiency of our perceptual feature set with an application in texture/pattern retrieval.
KeywordsWavelet Coefficient Color Pattern Wavelet Packet Texture Classification Markov Random Field
Unable to display preview. Download preview PDF.
- Amadsun, A. and King, R. (1989). Textural features corresponding to texture properties. SMC, 19:1264–1274.Google Scholar
- Brodatz, P. (1966) . Textures: A photographic album for artists and designers. New York: Dover. Google Scholar
- Do, M. N. and Vetterli, M. (2000). Texture similarity measurement using kullback-leibler distance on wavelet subbands. Proc. of IEEE International Conference on Image Processing.Google Scholar
- Healey, C. and Enns, J. T. (1998). Building perceptual textures to visualize multidimensional datasets. Proc. Visualization.Google Scholar
- Juleesz, B. and Bergen, J. R. (1983). Textons: The fundamental elements in preattentive vision and perception of textures. Bell System Technical Journal, 62:1619–1645.Google Scholar