Name
Abstract | ||
|---|---|---|
Attributes have shown great potential in visual recognition recently since they, as mid-level features, can be shared across different categories. However, existing attribute learning methods are prone to learning the correlated attributes which results in the difficulties of selecting attribute specific features. In this paper, we propose an attribute specific dictionary learning approach to address this issue. Category information is incorporated into our framework while learning the over-complete dictionary, which encourages the samples from the same category to have similar distributions over the dictionary bases. A novel scheme is developed to select the attribute specific dictionaries. The attribute specific dictionary consists of the bases which are only shared among the positive samples or the negative samples. The experiments on the Animals with Attributes (AwA) dataset show the effectiveness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2671188.2749337 | ICMR |
Keywords | Field | DocType |
Attribute Learning, Dictionary Learning, Dictionary Bases | Attribute learning,Dictionary learning,Pattern recognition,K-SVD,Computer science,Visual recognition,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
9 | 0.46 | 24 |
Authors | ||
3 | ||