Article

International Journal of Computer Vision

, Volume 80, Issue 1, pp 45-57

Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields

  • Jim MutchAffiliated withDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology Email author 
  • , David G. LoweAffiliated withDepartment of Computer Science, University of British Columbia

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Abstract

We investigate the role of sparsity and localized features in a biologically-inspired model of visual object classification. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. We refine the approach in several biologically plausible ways. Sparsity is increased by constraining the number of feature inputs, lateral inhibition, and feature selection. We also demonstrate the value of retaining some position and scale information above the intermediate feature level. Our final model is competitive with current computer vision algorithms on several standard datasets, including the Caltech 101 object categories and the UIUC car localization task. The results further the case for biologically-motivated approaches to object classification.

Keywords

Object class recognition Ventral visual pathway Sparsity Localized features