Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming

Brian Lam and Vic Ciesielski

Abstract. In this paper we show how genetic programming can be used to discover useful texture feature extraction algorithms. Grey level histograms of different textures are used as inputs to the evolved programs. One dimensional K-means clustering is applied to the outputs and the tightness of the clusters is used as the fitness measure. To test generality, textures from the Brodatz library were used in learning phase and the evolved features were used on classification problems based on the Vistex library. Using the evolved features gave a test accuracy of 74.8% while using Haralick features, the most commonly used method in texture classification, gave an accuracy of 75.5% on the same problem. Thus, the evolved features are competitive with those derived by human intuition and analysis. Furthermore, when the evolved features are combined with the Haralick features the accuracy increases to 83.2%, indicating that the evolved features are finding texture regularities not used in the Haralick approach.

LNCS 3103, p. 1114 ff.

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