Random projection in dimensionality reduction: applications to image and text data
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Video Google: A Text Retrieval Approach to Object Matching in Videos
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Distinctive Image Features from Scale-Invariant Keypoints
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Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
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ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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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.