Hybrid generative discriminative approaches based on Multinomial Scaled Dirichlet mixture models

Developing both generative and discriminative techniques for classification has achieved significant progress in the last few years. Considering the capabilities and limitations of both, hybrid generative discriminative approaches have received increasing attention. Our goal is to combine the advantages and desirable properties of generative models, i.e. finite mixture, and the Support Vector Machines (SVMs) as powerful discriminative techniques for modeling count data that appears in many domains in machine learning and computer vision applications. In particular, we select accurate kernels generated from mixtures of Multinomial Scaled Dirichlet distribution and its exponential approximation (EMSD) for support vector machines. We demonstrate the effectiveness and the merits of the proposed framework through challenging real-world applications namely; object recognition and visual scenes classification. Large scale datasets have been considered in the empirical study such as Microsoft MOCR, Fruits-360 and MIT places.