Texture Classification Using Dominant Wavelet Packet Energy Features

Authors:
Moon-Chuen Lee;Chi-Man Pun
Affiliations:
-;-
Venue:
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Year:
2000

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Abstract

This paper proposes a high performance texture classification method using dominant energy features from wavelet packet decomposition. We decompose the texture images with a family of real orthonnormal wavelet bases and compute the energy signatures using the wavelet packet coefficients. Then we select few number of most dominant energy values as features and employ a Mahalanobis distance classifier to classify a set of distinct natural textures selected from the Brodatz album. In our experiments, the proposed method employed a reduced feature set and involved less computation in classification time while still archiving high accuracy rate (94.8%) for classifying twenty classes of natural texture images.