Refining local descriptors by embedding semantic information for visual categorization
Pages 1381 - 1384
Abstract
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.
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Index Terms
- Refining local descriptors by embedding semantic information for visual categorization
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Information & Contributors
Information
Published In
November 2011
944 pages
ISBN:9781450306164
DOI:10.1145/2072298
- General Chairs:
- K. Selçuk Candan,
- Sethuraman Panchanathan,
- Balakrishnan Prabhakaran,
- Program Chairs:
- Hari Sundaram,
- Wu-Chi Feng,
- Nicu Sebe
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Published: 28 November 2011
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