@conference {13067,
	title = {Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance},
	booktitle = {Computer Vision (ICCV), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011/11//},
	pages = {161 - 168},
	abstract = {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.},
	keywords = {appearance, Birdlets;category, categorization;subordinate-level, detection;pose, detectors;pose-normalized, distinctions;shape, estimation;, extraction;pose, extraction;subordinate-level, information, model;salient, models;volumetric, pixels;part, poselet, primitives;computer, resolution;information, retrieval;object, scheme;volumetric, taxonomy;computer, vision;image},
	doi = {10.1109/ICCV.2011.6126238},
	author = {Farrell,R. and Oza,O. and Zhang,Ning and Morariu,V.I. and Darrell,T. and Davis, Larry S.}
}
