Attribute-based learning for large scale object classification

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2013 IEEE International Conference on Multimedia and Expo, 2013, pp. 1 - 6
Issue Date:
2013-01
Full metadata record
Files in This Item:
Filename Description Size
ATTRIBUTE-BASED LEARNING.pdfAccecpted manuscript316.91 kB
Adobe PDF
Scalability to large numbers of classes is an important challenge for multi-class classification. It can often be computationally infeasible at test phase when class prediction is performed by using every possible classifier trained for each individual class. This paper proposes an attribute-based learning method to overcome this limitation. First is to define attributes and their associations with object classes automatically and simultaneously. Such associations are learned based on greedy strategy under certain conditions. Second is to learn a classifier for each attribute instead of each class. Then, these trained classifiers are used to predict classes based on their attribute representations. The proposed method also allows trade-off between test-time complexity (which grows linearly with the number of attributes) and accuracy. Experiments based on Animals-with-Attributes and ILSVRC2010 datasets have shown that the performance of our method is promising when compared with the state-of-the-art.
Please use this identifier to cite or link to this item: