Papers by Manuel Dorantes
ceur-ws.org
Due to the amount of visual information that currently exists, there is a need to classify it pro... more Due to the amount of visual information that currently exists, there is a need to classify it properly. In this paper we present an alternative dual method for image categorization according to their texture content defined as GAF-SVM, this method is based in the use of Gabor Filters (GAF) 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 by taking a set of images containing coastal beach scenes and a set of images containing city scenes. 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 demonstrate the effectiveness of the proposed dual method by getting the error classification rate to near 9%.
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Papers by Manuel Dorantes