POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation

• Published in: 2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 955 - 962
• Main Authors: Berg, Thomas, Belhumeur, Peter N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2013
• Subjects: Accuracy; attributes; Birds; Face; face verification; Feature extraction; fine-grained visual categorization; Histograms; Image color analysis; part-based recognition; Training
• DOI: 10.1109/CVPR.2013.128
• ISSN: 1063-6919

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.