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Searching for objects driven by context

Bogdan Alexe, Nicolas Heess, Yee Whye Teh, Vittorio Ferrari
NIPS 2012
December 2012, in press

Abstract

The dominant visual search paradigm for object class detection is sliding windows. Although simple and effective, it is also wasteful, unnatural and rigidly hardwired.We propose strategies to search for objects which intelligently explore the space of windows by making sequential observations at locations decided based on previous observations. Our strategies adapt to the class being searched and to the content of a particular test image, exploiting context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set. In addition to being more elegant than sliding windows, we demonstrate experimentally on the PASCAL VOC 2010 dataset that our strategies evaluate two orders of magnitude fewer windows while achieving higher object detection performance.


Link to publisher's page
@InProceedings{eth_biwi_00980,
  author = {Bogdan Alexe and Nicolas Heess and Yee Whye Teh and Vittorio Ferrari},
  title = {Searching for objects driven by context},
  booktitle = {NIPS 2012},
  year = {2012},
  month = {December},
  keywords = {object detection, object recognition},
  note = {in press}
}