Automatic construction of image classification algorithms based on genetic image network

Automatic construction methods for image processing proposed till date approximate adequate image transformation from original images to their target images using a combination of several known image processing filters by evolutionary computation techniques. Genetic Image Network (GIN) is a recent automatic construction method for image transformation. The representation of GIN is a network structure. In this paper, we propose a method of automatic construction of image classifiers based on GIN, designated as Genetic Image Network for Image Classification (GIN-IC). The representation of GIN-IC is a feed-forward network structure. GIN-IC is composed of image transformation nodes, feature extraction nodes, and arithmetic operation nodes. GIN-IC transforms original images to easier-to-classify images using image transformation nodes, and selects adequate image features using feature extraction nodes. We apply GIN-IC to test problems involving multi-class categorization of texture images and two-class categorization of pedestrian and non-pedestrian images. Experimental results show that the use of image transformation nodes is effective for image classification problems.