The Truth about Cats and Dogs

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 i...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Omkar M Parkhi, Andrea Vedaldi, C. V. Jawahar, Andrew Zisserman
• Keywords: Cats And Dogs, Computer Vision, Deformable Object, ICCV 2011, Object Categories