Learning discriminative visual dictionary for natural scene categorization

Many successful systems for scene recognition transform low-level descriptors into complex representations. This process consists of the two steps: 1) feature coding, which performs a pointwise transformation of the descriptors into a representation adapted to the task, and 2) image pooling, which summarizes the coded features. Even though these two steps have been paid so much attention, but there are still some problems in combining scene semantic with local features. The goal of this paper is threefold: to address the problem by modifying the traditional bag-of-features (BoF) framework; to show how to achieve the best performance by learning a semi-supervised discriminative dictionary; and to provide theoretical and empirical insight into the remarkable performance. By teasing apart components shared by modern scene categorization pipeline, our approach aims to facilitate the design of better scene recognition architectures.