Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 54, Issue 9, Pages 1373-1387Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2013.06.003
Keywords
Multi-label classification; Automatic image annotation; Neighborhood rough sets
Categories
Funding
- National Natural Science Foundation of China [61075056, 61273304, 61103067, 61202170]
- Fundamental Research Funds for the Central Universities
- State Scholarship Fund of China [201206260047]
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Automatic image annotation is concerned with the task of assigning one or more semantic concepts to a given image. It is a typical multi-label classification problem. This paper presents a novel multi-label classification framework MLNRS based on neighborhood rough sets for automatic image annotation which considers the uncertainty of the mapping from visual feature space to semantic concepts space. Given a new instances, its neighbors in the training set are firstly identified. After that, based on the concept of upper and lower approximations of neighborhood rough sets, all possible labels of the given instance are found. Then, based on the statistical information gained from the label sets of the neighbors, maximum a posteriori (MAP) principle is utilized to determine the label set for the given instance. Experiments completed for three different image datasets show that MLNRS achieves more promising performance in comparison with to some well-known multi-label learning algorithms. (C) 2013 Elsevier Inc. All rights reserved.
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