Introduction to the special issue on learning from multi-label data
Machine Learning
An efficient two-stage framework for image annotation
Pattern Recognition
Multi-label image annotation based on multi-model
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Support vector description of clusters for content-based image annotation
Pattern Recognition
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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.