TY - CONF T1 - Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance T2 - Computer Vision (ICCV), 2011 IEEE International Conference on Y1 - 2011 A1 - Farrell,R. A1 - Oza,O. A1 - Zhang,Ning A1 - Morariu,V.I. A1 - Darrell,T. A1 - Davis, Larry S. KW - appearance KW - Birdlets;category KW - categorization;subordinate-level KW - detection;pose KW - detectors;pose-normalized KW - distinctions;shape KW - estimation; KW - extraction;pose KW - extraction;subordinate-level KW - information KW - model;salient KW - models;volumetric KW - pixels;part KW - poselet KW - primitives;computer KW - resolution;information KW - retrieval;object KW - scheme;volumetric KW - taxonomy;computer KW - vision;image AB - 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 with corresponding volumetric part location, scale and orientation parameters. These parameters implicitly define a mapping from the image pixels into a pose-normalized appearance space, removing view and pose dependencies, facilitating fine-grained categorization from relatively few training examples. JA - Computer Vision (ICCV), 2011 IEEE International Conference on M3 - 10.1109/ICCV.2011.6126238 ER -