Scene classification by fuzzy local moments

The identification of images irrespective of their location, size and orien tation is one of the important tasks in pattern analysis. The use of global moment features has been one of the most popular techniques for this purpo se. We present a simple and effective method for gray-level image represent ation and identification which utilizes fuzzy radial moments of image segme nts (local moments) as features as opposed to global features. A multilayer perceptron neural network is employed for classification. Fuzzy entropy me asure 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 advanta ge for real-time applications. The classification capability and robustness of the technique are demonstrated by experiments on scaled, rotated and no isy gray-level images of uppercase and lowercase characters and digits of E nglish alphabet, as well as the images of a set of tools. The proposed appr oach can handle rotation, scale and translation invariance, noise and fuzzi ness simultaneously.