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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: SPIE -int society optical engineering
Languages: English
Types: Unknown
Subjects: QC
Wavelet packets are well-known for their ability to compactly represent textures consiting of oscillatory patterns such as fingerprints or striped cloth. In this paper, we report recent work on representing both periodic and granular types of texture using adaptive wavelet basis functions. The discrimination power of a wavelet packet subband can be defined as its ability to differentiate between any two texture classes in the transform domain, consequently leading to better classification results. The problem of adaptive wavelet basis selection for texture analysis can, therefore, be solved by using a dynamic programming approach to find the best basis from a library of orthonormal basis functions with respect to a discriminant measure. We present a basis selection algorithm which extends the concept of 'Local Discrminant Basis' (Saito and Coifman, 1994) to two dimensions. The problem of feature selection is addressed by sorting the features according to their relevance as described by the discriminant measure, which has a significant advantage over other feature selection methods that both basis selection and reduction of dimensionality of the feature space can be done simultaneously. We show that wavelet packets are good at representing not only oscillatory patterns but also granular textures. Comparative results are presented for four different distance metrics: Kullback-Leibler (KL) divergence, Jensen-Shannon (JS) divergence, Euclidean distance, and Hellinger distance. Initial experimental results show that Hellinger and Euclidean distance metrics may perform better as compared to other cost functions.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • [8] F.G. Meyer and J. Chinrunreung. Analysis of event-related fmri data using local clustering bases. IEEE Trans. on Medical Imaging, 2003. to appear.
    • [9] N.M. Rajpoot. Texture Classi cation Using Discriminant Wavelet Packet Subbands. In Proc. IEEE Midwest Syposium on Circuits and Systems, Aug. 2002.
    • [10] T. Randen and J.H. Husoy. Filtering for texture classi cation: A comparative study. IEEE Trans. on PAMI, 21(4), April 1999.
    • [11] N. Saito, R. Coifman, F. B. Geshwind, and F. Warner. Discriminant Feature Extraction Using Empirical Probability Density Estimation and a Local Basis Library. Pattern Recognition, 35:2841{2852, 2002.
    • [12] N. Saito and R.R. Coifman. Local discriminant bases. In A.F. Laine and M.A. Unser, editors, Mathematical Imaging: Wavelet Applications in Signal and Image Processing II, volume 2303, 1994.
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