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Animal detection in natural scenes: Critical features revisited

2010

Article

ei


S. J. Thorpe, D. Fize, and C. Marlot (1996) showed how rapidly observers can detect animals in images of natural scenes, but it is still unclear which image features support this rapid detection. A. B. Torralba and A. Oliva (2003) suggested that a simple image statistic based on the power spectrum allows the absence or presence of objects in natural scenes to be predicted. We tested whether human observers make use of power spectral differences between image categories when detecting animals in natural scenes. In Experiments 1 and 2 we found performance to be essentially independent of the power spectrum. Computational analysis revealed that the ease of classification correlates with the proposed spectral cue without being caused by it. This result is consistent with the hypothesis that in commercial stock photo databases a majority of animal images are pre-segmented from the background by the photographers and this pre-segmentation causes the power spectral differences between image categories and may, furthermore, help rapid animal detection. Data from a third experiment are consistent with this hypothesis. Together, our results make it exceedingly unlikely that human observers make use of power spectral differences between animal- and no-animal images during rapid animal detection. In addition, our results point to potential confounds in the commercially available “natural image” databases whose statistics may be less natural than commonly presumed.

Author(s): Wichmann, FA. and Drewes, J. and Rosas, P. and Gegenfurtner, K.
Journal: Journal of Vision
Volume: 10
Number (issue): 4
Pages: 1-27
Year: 2010
Month: April
Day: 15

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1167/10.4.6

Links: Web

BibTex

@article{WichmannDRG2010,
  title = {Animal detection in natural scenes: Critical features revisited},
  author = {Wichmann, FA. and Drewes, J. and Rosas, P. and Gegenfurtner, K.},
  journal = {Journal of Vision},
  volume = {10},
  number = {4},
  pages = {1-27},
  month = apr,
  year = {2010},
  month_numeric = {4}
}