POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation
- 1 June 2013
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 955-962
- https://doi.org/10.1109/cvpr.2013.128
Abstract
From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs.-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.Keywords
This publication has 20 references indexed in Scilit:
- Face-recognition-based dog-breed classification using size and position of each local part, and PCAPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- An associate-predict model for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Combining randomization and discrimination for fine-grained image categorizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Describable Visual Attributes for Face Verification and Image SearchIEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
- Learning to detect unseen object classes by between-class attribute transferPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Automated Flower Classification over a Large Number of ClassesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Face recognitionACM Computing Surveys, 2003
- Multiresolution gray-scale and rotation invariant texture classification with local binary patternsIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Object recognition from local scale-invariant featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- Objects, parts, and categories.Journal of Experimental Psychology: General, 1984