PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Truth About Cats and Dogs
O Parkhi, Andrea Vedaldi, C.V. Jawahar and Andrew Zisserman
In: ICCV 2011, 6-13 November 2011, Barcelona.

Abstract

Template-based object detectors such as the deformable parts model of Felzenszwalb et al. [11] achieve state-ofthe- art performance for a variety of object categories, but are still outperformed by simpler bag-of-words models for highly flexible objects such as cats and dogs. In these cases we propose to use the template-based model to detect a distinctive part for the class, followed by detecting the rest of the object via segmentation on image specific information learnt from that part. This approach is motivated by two observations: (i) many object classes contain distinctive parts that can be detected very reliably by template-based detectors, whilst the entire object cannot; (ii) many classes (e.g. animals) have fairly homogeneous coloring and texture that can be used to segment the object once a sample is provided in an image. We show quantitatively that our method substantially outperforms whole-body template-based detectors for these highly deformable object categories, and indeed achieves accuracy comparable to the state-of-the-art on the PASCAL VOC competition, which includes other models such as bagof- words.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:8319
Deposited By:Sunando Sengupta
Deposited On:20 October 2011