Reference Regions for Image Classification

Due to the amount of visual information that currently exists, there is a need to classify it. In this paper we present an alternative method for image categorization according to their texture content using Gabor Filters and Support Vector Machine (SVM). To perform the image classification we rely on filtering techniques for feature extraction mixed with statistical learning techniques to perform the data separation. The experiments were carried out using up to six different sets of images, Including rocky canyons, shore lines, among others. A feature vector is obtained from applying a bank of Gabor Filters to the input images, the output feature space is then used as an input to the SVM Classifier. The Support Vector Machine is responsible for learning a model that is capable of separating the sets of input images. Experimental results show the effectiveness of the proposed dual method by getting the error classification rate to near 9%.