Supervised learning algorithms for visual object categorization

This thesis presents novel techniques for image recognition systems for better understanding image content. More specifically, it looks at the algorithmic aspects and experimental verification to demonstrate the capability of the proposed algorithms. These techniques aim to improve the three major components that are part of current state-of-the-art image recognition systems. This thesis offers four algorithms implementing different strategies to effectively classify images into correct categories automatically. A set of images from all categories is selected, labeled and next become learning data to learn models to categorize other images. To show the effectiveness of the proposed algorithms, all approaches were validated on several standard datasets namely PASCAL 2006 and 2007, Caltech-101 and Corel. Each proposed algorithm is explained in detail in separate chapters in this thesis.