Special issue on pattern recognition and information processing using neural networks

Abstract

Neural network techniques have proven to be flexible in pattern recognition and information processing in complex environments. They typically include BP networks, RBF networks, support vector machine (SVM) and other similar biologically motivated models. The neural network techniques are able to enhance recognition accuracy, and have found applications in real-world environments. This special issue addresses neural network techniques in pattern recognition and information processing problems. The first paper ‘‘Kernel based improved discriminant analysis and its application to face recognition,’’ coauthored by Dake Zhou and Zhenmin Tang, presents a variant of KDA called kernel-based improved discriminant analysis (KIDA). In the proposed framework, original samples are projected firstly into a feature space by an implicit nonlinear mapping. After reconstructing betweenclass scatter matrix in the feature space by weighted schemes, the kernel method is used to obtain a modified Fisher criterion. Finally, simultaneous diagonalization technique is employed to find lower dimensional nonlinear features with significant discriminant power. The second paper ‘‘A Novel Application of a Self-Organizing Network for Recognition of Facial Expressions from Contours,’’ coauthored by W.F. Gu, Y.V. Venkatesh, and C. Xiang, proposes a self-organizing network for recognizing facial expressions using biologically plausible features: contours of face and its components. Experimental results show that the recognition accuracy of the presented algorithm is superior to that of other algorithms in the literature on the Japanese Female Facial Expression (JAFFE) database. The third paper ‘‘An Automatic Fuzzy C-Means Algorithm for Image Segmentation,’’ co-authored by Yanling Li and Yi Shen, proposes a novel fuzzy clustering algorithm for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known a priori. The new algorithm initiates the first two centroids of clusters by a method based on hard c-means algorithm and automatically determines the appropriate cluster number for image segmentation. The fourth paper ‘‘Content Based Image Classification with Wavelet Relevance Vector Machines,’’ co-authored by Arvind Tolambiya, S. Venkataraman, and Prem K. Kalra, introduces the use of relevance vector machines (RVM) for content-based image classification and compares it with the conventional SVM approach. The authors also propose a new wavelet-based feature extraction method that extracts less number of features as compared to other wavelet-based feature extraction methods. The fifth paper ‘‘A Three-layer Back-propagation Neural Network for Spam Detection Using Artificial Immune Concentration,’’ co-authored by Guangchen Ruan and Ying Tan, uses a three-layer back-propagation neural network F.-C. Sun (&) State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, 100084 Beijing, China e-mail: fcsun@tsinghua.edu.cn