A supervised neural network approach to invariant image recognition

Invariant image recognition is one of the hardest problems in computer vision. The aim is to identify an image independently of its rotational orientation and size, as well as changing its color intensity. The current techniques such as high-ordered neural network and Zernike moments are not practical to apply to color images of size at least 256 /spl times/ 256 pixels. This paper introduces a novel recognition system, which has less computational time and space than the current techniques. Most implementations for finding machine classifiers are the multi layer perceptron (MLP) of which drawbacks lead to its costly training time and its slow learning behavior. The proposed system builds the classification function based on the concept of support vector machine (SVM) without kernel function of k-NN, called critical support vector machine (CSVM). CSVM is better generalization ability in terms of accuracy rate and lower complexity in terms of computational time and space. In part of invariance, the paper develops the rotational and scaling invariant self-organizing mapping (RSISOM), which composes the Kohonen's learning concept and principle component analysis. RSISOM can capture the features of color images invariant to rotation, scaling and color intensity. In experiment, the proposed system is tested against different 34 colored images with 98.2 % of the correct classification.