Hierarchical feature coding for image classification

Feature coding and pooling are two critical stages in the widely used Bag-of-Features (BOF) framework in image classification. After coding, each local feature formulates its representation by the visual codewords. However, the two-dimensional feature-code layout is transformed to a one-dimensional codeword representation after pooling. The property for each local feature is ignored and the whole representation is tightly coupled. To resolve this problem, we propose a hierarchical feature coding approach which regards each feature-code representation as a high level feature. Codeword learning, coding and pooling are also applied to these new features, and thus a high level representation of the image is obtained. Experiments on different datasets validate our analysis and demonstrate that the new representation is more discriminative than that in the previous BOF framework. Moreover, we show that various kinds of traditional feature coding algorithms can be easily embedded into our framework to achieve better performance.