標題: Effective Semantic Annotation by Image-to-Concept Distribution Model
作者: Su, Ja-Hwung
Chou, Chien-Li
Lin, Ching-Yung
Tseng, Vincent S.
資訊工程學系
Department of Computer Science
關鍵字: Entropy;image annotation;image-to-concept distribution;tf-idf
公開日期: 1-Jun-2011
摘要: 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.
URI: http://dx.doi.org/10.1109/TMM.2011.2129502
http://hdl.handle.net/11536/8797
ISSN: 1520-9210
DOI: 10.1109/TMM.2011.2129502
期刊: IEEE TRANSACTIONS ON MULTIMEDIA
Volume: 13
Issue: 3
起始頁: 530
結束頁: 538
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