This paper describes a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.

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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.

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