Patch-based within-object classification

  title={Patch-based within-object classification},
  author={Jania Aghajanian and Jonathan Warrell and Simon Prince and Peng Li and Jennifer L. Rohn and Buzz Baum},
  journal={2009 IEEE 12th International Conference on Computer Vision},
Advances in object detection have made it possible to collect large databases of certain objects. In this paper we exploit these datasets for within-object classification. For example, we classify gender in face images, pose in pedestrian images and phenotype in cell images. Previous work has mainly targeted the above tasks individually using object specific representations. Here, we propose a general Bayesian framework for within-object classification. Images are represented as a regular grid… CONTINUE READING
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