refining local descriptors by embedding semantic information for visual categorization
Local descriptor extraction and vector quantization are the important components of widely-used Bag-of-Features (BoF) model for visual categorization. This paper proposes a simple and efficient approach to refine the local descriptors for vector quantization by embedding semantic information. The original local descriptors are integrated by a sequence of category-independent and category-dependent basis. Particularly, the category-dependent basis is learned by minimizing the joint loss minimization over local descriptors from different categories with a shared regularization penalty, which can be formulated as a linear programming problem. The transferred descriptors are further quantized and aggregated to the visual vocabulary. Experiments are performed on PASCAL VOC 2007 benchmark and the quantitative comparisons with several state-of-the-art approaches demonstrate the effectiveness of our proposed approach.
local descriptor semantic information visual categorization