Computer Vision, IEEE International Conference on (2005)
Oct. 17, 2005 to Oct. 20, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2005.16
Ruofei Zhang , State University of New York at Binghamton
Zhongfei (Mark) Zhang , State University of New York at Binghamton
Mingjing Li , Microsoft Research Asia
Wei-Ying Ma , Microsoft Research Asia
Hong-Jiang Zhang , Microsoft Research Asia
This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7,736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature.
Z. (. Zhang, H. Zhang, M. Li, W. Ma and R. Zhang, "A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieva," Computer Vision, IEEE International Conference on(ICCV), Beijing, China, 2005, pp. 846-851.