Content-based image classification using a neural network

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In this paper, we propose a method of content-based image classification using a neural network. The images for classification are object images that can be divided into foreground and background. To deal with the object images efficiently, in the preprocessing step we extract the object region using a region segmentation technique. Features for the classification are shape-based texture features extracted from wavelet-transformed images. The neural network classifier is constructed for the features using the back-propagation learning algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 300 training data and 300 test data composed of 10 images from each of 30 classes shows classification rates of 81.7% and 76.7% correct, respectively.