Scene Classification by Fuzzy Local Moments

Identiication of images irrespective of their location, size and orientation is one of the important tasks in pattern analysis. Use of global moment features has been one of the most popular techniques for this purpose. We present a simple and eeective method for gray-level image representation and identiication which utilizes fuzzy radial moments of image segments (local moments) as features as opposed to global features. A multi-layer perceptron neural network is employed for classiication. Fuzzy entropy measure is applied to optimize the parameters of the membership function. The technique does not require translation, scaling or rotation of the image. Furthermore, it is suitable for parallel implementation which is an advantage for real-time applications. The classiication capability and robustness of the technique is demonstrated by experiments on scaled, rotated and noisy gray-level images of uppercase and lowercase characters and digits of English alphabet, as well as the images of a set of tools.