Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 13, Issue 3, Pages 530-538Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2011.2129502
Keywords
Entropy; image annotation; image-to-concept distribution; tf-idf
Categories
Funding
- National Science Council, Taiwan, R.O.C. [NSC99-2631-H-006-002, NSC99-2218-E-006-001]
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Image annotation based on visual features has been a difficult problem due to the diverse associations that exist between visual features and human concepts. In this paper, we propose a novel approach called Annotation by Image-to-Concept Distribution Model (AICDM) for image annotation by discovering the associations between visual features and human concepts from image-to-concept distribution. Through the proposed image-to-concept distribution model, visual features and concepts can be bridged to achieve high-quality image annotation. In this paper, we propose to use visual features, models, and visual genes which represent analogous functions to the biological chromosome, DNA, and gene. Based on the proposed models using entropy, tf-idf, rules, and SVM, the goal of high-quality image annotation can be achieved effectively. Our empirical evaluation results reveal that the AICDM method can effectively alleviate the problem of visual-to-concept diversity and achieve better annotation results than many existing state-of-the-art approaches in terms of precision and recall.
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