Image recognition using a growing-cell-based neural network
Jinming Yang, Majid A. Ahmadi, GRAHAM A. JULLIEN, William Cameron Miller · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
In this paper a new approach to image recognition using feature extraction based on a revised nearest neighbor clustering method is described. A set of candidate feature vectors are formed by using the Gabor transform of the sample image to compute a number of Gabor kernels with different frequency and orientation parameters. Each of the candidate feature vectors is then sequentially inputted to a self- organizing neural network architecture that is used in conjunction with a revised nearest-neighbor algorithm. The revised nearest-neighbor method assigns an input vector to the nearest prototype (code book vector) when the distance between them is found to be within a preset threshold, and creates a new prototype when the distance is larger than the preset threshold value. The distance computation is conducted by measuring the saliency among the vectors of interest, which differs from traditional norms (e.g. Euclidean norm). Simulation results show that the proposed method is efficient in extracting feature vectors from images. These feature vectors are representative of the image and can be applied to image identification. The novelty associated with this work lies in the use of the saliency of feature vectors as the distance norm and a growing cell self-organizing structure to capture the feature vectors.