Birdlets: Subordinate Categorization Using Volumetric Primitives and Pose-Normalized Appearance

Subordinate-level categorization typically rests on establishing salient distinctions between part-level characteristics of objects, in contrast to basic-level categorization, where the presence or absence of parts is determinative. We develop an approach for subordinate categorization in vision, focusing on an avian domain due to the fine-grained structure of the category taxonomy for this domain. We explore a pose-normalized appearance model based on a volumetric poselet scheme. The variation in shape and appearance properties of these parts across a taxonomy provides the cues needed for subordinate categorization. Training pose detectors requires a relatively large amount of training data per category when done from scratch; using a subordinate-level approach, we exploit a pose classifier trained at the basic-level, and extract part appearance and shape information to build subordinate-level models. Our model associates the underlying image pattern parameters used for detection w...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Ryan Farrell, Om Oza, Ning Zhang, Vlad I. Morariu, Trevor Darrell, Larry S. Davis
• Keywords: Appearance Model, Computer Vision, ICCV 2011, Image Pattern, Subordinate Level