Complex-Wavelet Structural Similarity Based Image Classification

Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in image classification problems has not been deeply investi- gated. In this study, we introduce a series of novel image classification algorithms based on CW-SSIM and use handwritten digit and face image recognition as examples for demonstration, including CW-SSIM based nearest neighbor method, CW-SSIM based k means method, CW-SSIM based support vector machine method (SVM) and CW-SSIM based SVM using affinity propagation. Among the proposed approaches, the best compromise between accuracy and complexity is obtained by the CW-SSIM support vector machine algorithm, which combines an unsupervised clustering method to divide the training images into clusters with representative images and a supervised learning method based on support vector machines to maximize the classification accuracy. Our experiments show that such a conceptually simple image classification method, which does not involve any registration, intensity normalization or sophisticated feature extraction processes, and does not rely on any modeling of the image patterns or distortion processes, achieves competitive performance with reduced computational cost

A Numeral Character Recognition Using the PCA Mixture Model

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A numeral character recognition using the PCA mixture model

This paper proposes a method for recognizing the numeral characters based on the PCA (Principal Component Analysis) mixture model. The proposed method is motivated by the idea that the classification accuracy is improved by modeling each class into a mixture of several components and by performing the classification in the compact and decorrelated feature space. For realizing the idea, each numeral class is partitioned into several clusters and each cluster's density is estimated by a Gaussian distribution function in the PCA transformed space. The parameter estimation is performed by an iterative EM (Expectation Maximization) algorithm, and model order is selected by a fast sub-optimal validation scheme. The proposed method is also computation-effective because the optimal feature components for a cluster are determined by a sequential elimination of insignificant feature due to the ordering property of the significance among the feature components in the PCA transformed space. Simulation results shows that the proposed recognition method outperforms other methods such as the k-NN (Nearest Neighbor) method, a single PCA model, or the ICA (Independent Component Analysis) mixture model in terms of recognition accuracy. (C) 2002 Elsevier Science B.V. All rights reserved.X1116sciescopu