Automatic Discovery and Optimization of Parts for Image Classification

@article{Parizi2014AutomaticDA,
  title={Automatic Discovery and Optimization of Parts for Image Classification},
  author={Sobhan Naderi Parizi and Andrea Vedaldi and Andrew Zisserman and Pedro F. Felzenszwalb},
  journal={CoRR},
  year={2014},
  volume={abs/1412.6598}
}
Abstract: Part-based representations have been shown to be very useful for image classification. Learning part-based models is often viewed as a two-stage problem. First, a collection of informative parts is discovered, using heuristics that promote part distinctiveness and diversity, and then classifiers are trained on the vector of part responses. In this paper we unify the two stages and learn the image classifiers and a set of shared parts jointly. We generate an initial pool of parts by… CONTINUE READING

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