Multitask Learning of Compact Semantic Codebooks for Context-Aware Scene Modeling
In the past few decades, we have witnessed the success of bag-of-features (BoF) models in scene classification, object detection, and image segmentation. Whereas it is also well acknowledged that the limitation of BoF-based methods lies in the low-level feature encoding and coarse feature pooling. This paper proposes a novel scene classification method, which leverages several semantic codebooks learned in a multitask fashion for robust feature encoding, and designs a context-aware image representation for efficient feature pooling. Apart from conventional universal codebook learning approaches, the proposed method encodes each class of local features with a unique semantic codebook, which captures the distinct distribution of different semantic classes more effectively. Instead of learning each semantic codebook separately, we learn a compact global codebook, of which each semantic codebook is a sparse subset, with a two-stage iterative multitask learning algorithm. While minimizing the clustering divergence, the semantic codeword assignment is solved by submodular optimization simultaneously. Built upon the global and semantic codebooks, a context-aware image representation is further developed to encode both global and semantic features in image representation via contextual quantization, semantic response computation, and semantic pooling. Extensive experiments have been conducted to validate the effectiveness of the proposed method on various public benchmarks with several popular local features.