Abstract
Nowadays classification of images into labeled multi-classes is one of the major research problems. In the field of artificial intelligence, the Histogram of Oriented Gradients (HOG) is employed for extracting features to identify which class a particular image belongs to. HOG counts the occurrence of gradient orientations in localized sections of an image. HOG features contain information about both edge and its direction other than edge detection which only contains edge information. Based on HOG features we classified images in the given dataset using Error-Correcting Output Codes (ECOC) based multi-class Support Vector Machine (SVM) classifier. The performance of ECOC-based multi-class SVM is compared with Selection based on Accuracy Intuition and Diversity algorithm (SAID) to find out the outperforming classifier over the given dataset. Based on confusion metrics, it is observed that ECOC-based multi-class SVM performs better than the SAID algorithm.
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