Sparse Spatial Coding: A Novel Approach to Visual Recognition

@article{Oliveira2014SparseSC,
  title={Sparse Spatial Coding: A Novel Approach to Visual Recognition},
  author={Gabriel L. Oliveira and Erickson Rangel do Nascimento and Ant{\^o}nio Wilson Vieira and Mario Fernando Montenegro Campos},
  journal={IEEE Transactions on Image Processing},
  year={2014},
  volume={23},
  pages={2719-2731}
}
Successful image-based object recognition techniques have been constructed founded on powerful techniques such as sparse representation, in lieu of the popular vector quantization approach. However, one serious drawback of sparse space-based methods is that local features that are quite similar can be quantized into quite distinct visual words. We address this problem with a novel approach for object recognition, called sparse spatial coding, which efficiently combines a sparse coding… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 52 references

Global Gaussian approach for scene categorization using information geometry

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition • 2010
View 4 Excerpts
Highly Influenced

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) • 2006
View 15 Excerpts
Highly Influenced

The importance of encoding versus training with sparse coding and vector quantization

A. Coates, N. Andrew
Proc. ICML, 2011, pp. 921–928. • 2011
View 3 Excerpts
Highly Influenced

Local features are not lonely – Laplacian sparse coding for image classification

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition • 2010
View 4 Excerpts
Highly Influenced

Linear spatial pyramid matching using sparse coding for image classification

2009 IEEE Conference on Computer Vision and Pattern Recognition • 2009
View 11 Excerpts
Highly Influenced

Multi Kernel Learning with Online-Batch Optimization

Journal of Machine Learning Research • 2012
View 1 Excerpt

An analysis of single-layer networks in unsupervised feature learning

A. Coates, H. Lee, N. Andrew
Proc. AISTATS, 2011. • 2011
View 1 Excerpt

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