Many image features for image recognition have been proposed in previous studies. The dimensionality of the previously-proposed image features is mostly over several thousands. The higher the dimensionality of features, the higher the accuracy of image classification and object recognition. However, the use of these high dimensional features involves high computational costs and the difficulty of analysis of recognition processing. In this paper, we propose a hierarchical feature dimension reduction method which is based on cartesian genetic programming (CGP). The proposed method generates a predefined number of new features one at a time using CGP per layer. A CGP generates a new feature by combining the previously-proposed image features. The proposed method finally generates three-dimensional features to visualize the data landscapes. We performed experiments on the Graz dataset, the capsule endoscopy images and the INRIA person dataset, and with regard to the support vector machine, the decision tree and k-nearest neighbor classifiers, the classification results obtained using the features generated by the proposed method were better than those using the previously-proposed features. The visualization results of the three-dimensional features generated by the proposed method showed that the object images are broadly separated from the non-object images. Moreover, the proposed method reduced the computational costs of feature extraction and classification by reducing the dimensionality of features.
2015 The Japanese Society for Evolutionary Computation