Context and subcategories for sliding window object recognition

@inproceedings{Hebert2012ContextAS,
  title={Context and subcategories for sliding window object recognition},
  author={Martial Hebert and Alexei A. Efros and Santosh Kumar Divvala},
  year={2012}
}
Object recognition is one of the fundamental challenges in computer vision, where the goal is to identify and localize the extent of object instances within an image. The current de facto standard for building high-performance object category detectors is the sliding window approach. This approach involves scanning an image with a fixed-size rectangular window and applying a classifier to the features extracted within the sub-image defined by the window. In this thesis, we study two important… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 164 REFERENCES

Histograms of oriented gradients for human detection

  • 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

SUN database: Large-scale scene recognition from abbey to zoo

  • 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 2010
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Decomposing a scene into geometric and semantically consistent regions

  • 2009 IEEE 12th International Conference on Computer Vision
  • 2009
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

A discriminatively trained, multiscale, deformable part model

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition
  • 2008
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Using Segmentation to Verify Object Hypotheses

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition
  • 2007
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Recovering Surface Layout from an Image

  • International Journal of Computer Vision
  • 2006
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

On the semantics of a glance at a scene

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

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