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Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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LIBLINEAR: A Library for Large Linear Classification
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Exploring relations of visual codes for image classification
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Salient coding for image classification
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Local feature coding has drawn much attention in recent years. Many excellent coding algorithms have been proposed to improve the bag-of-words model. This paper proposes a new local feature coding method called local hypersphere coding (LHC) which possesses two distinctive differences from traditional coding methods. Firstly, we describe local features by the edges between visual words. Secondly, the reconstruction center is moved from the origin to the nearest visual word, thus feature coding is performed on the hypersphere of feature space. We evaluate our coding method on several benchmark datasets for image classification. The experimental results of the proposed method outperform several state-of-the-art coding methods, indicating the effectiveness of our method.