Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance

TitleBirdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance
Publication TypeConference Papers
Year of Publication2011
AuthorsFarrell R, Oza O, Zhang N, Morariu VI, Darrell T, Davis LS
Conference NameComputer Vision (ICCV), 2011 IEEE International Conference on
Date Published2011/11//
Keywordsappearance, 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

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.