- Author
- Year
- 2015
- Title
- Attributes and Categories for Generic Instance Search from One Example
- Event
- 2015 IEEE Conference on Computer Vision and Pattern Recognition
- Book/source title
- 2015 IEEE Conference on Computer Vision and Pattern Recognition: 7-12 June 2015, Boston, MA
- Pages (from-to)
- 177-186
- Publisher
- Piscataway, NJ: IEEE
- ISBN
- 9781467369640
- Document type
- Conference contribution
- Faculty
- Faculty of Science (FNWI)
- Institute
- Informatics Institute (IVI)
- Abstract
-
This paper aims for generic instance search from one example where the instance can be an arbitrary 3D object like shoes, not just near-planar and one-sided instances like buildings and logos. Firstly, we evaluate state-of-the-art instance search methods on this problem. We observe that what works for buildings loses its generality on shoes. Secondly, we propose to use automatically learned category-specific attributes to address the large appearance variations present in generic instance search. On the problem of searching among instances from the same category as the query, the category-specific attributes outperform existing approaches by a large margin. On a shoe dataset containing 6624 shoe images recorded from all viewing angles, we improve the performance from 36.73 to 56.56 using category-specific attributes. Thirdly, we extend our methods to search objects without restricting to the specifically known category. We show the combination of category-level information and the category-specific attributes is superior to combining category-level information with low-level features such as Fisher vector.
- URL
- go to publisher's site
- Language
- English
- Persistent Identifier
- https://hdl.handle.net/11245/1.488843
- Downloads
-
TaoCVPR2015(Submitted manuscript)
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