The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance
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.
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Metadata
- Authors
- Farrell, R., Oza, O., Zhang, N., Morariu, V. I., Darrell, T., & Davis, L. S.
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 161-168
- OpenAlex
- W2110765924
- Cited by
- 195
- References
- 10
- FWCI
- 13.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Farrell, R., Oza, O., Zhang, N., Morariu, V. I., Darrell, T., & Davis, L. S. (2011). Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance. 2011 International Conference on Computer Vision. https://doi.org/10.1109/iccv.2011.6126238
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