Beyond bag of words: Combining generative and discriminative models for natural scene categorization

This paper proposes a simple yet new and effective framework by combining generative model and discriminative model for natural scene categorization. A state-of-the-art approach for scene categorization is the Bag-of-Words (BoW) framework. However, there exist many categories in natural scenes. Often when a new category is considered, the codebook in BoW framework needs to be re-generated, which will involve exhaustive computation. In view of this, this paper tries to ad dress the issue by designing a new framework with the ability of incremental learning. When an additional category is considered, much lower computational cost is needed while the resulting image signatures are still discriminative. The image signatures for training discriminative model are carefully de signed based on the generative model. The effectiveness of the proposed method is validated on UIUC Scene-15 dataset and it is shown to outperform the state-of-the-art method in BoW framework for scene categorization.