Designing Category-Level Attributes for Discriminative Visual Recognition
Abstract
Attribute-based representation has shown great promises for visual recognition due to its intuitive interpretation and cross-category generalization property. However, human efforts are usually involved in the attribute designing process, making the representation costly to obtain. In this paper, we propose a novel formulation to automatically design discriminative category level attributes, which can be efficiently encoded by a compact category-attribute matrix. The formulation allows us to achieve intuitive and critical design criteria (category-separability, learnability) in a principled way. The designed attributes can be used for tasks of cross-category knowledge transfer, achieving superior performance over well-known attribute dataset Animals with Attributes (AwA) and a large-scale ILSVRC2010 dataset (1.2M images). This approach also leads to state-of-the-art performance on the zero-shot learning task on AwA.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 23, 2013
- Accession Number
- AD1175045
Entities
People
- Felix X. Yu
- John R. Smith
- Liangliang Cao
- Rogerio S. Feris
- Shih-fu Chang
Organizations
- Columbia University
- International Business Machines Corporation (Armonk, NY)