Academic
Publications
A probabilistic semantic model for image annotation and multi-modal image retrieval

A probabilistic semantic model for image annotation and multi-modal image retrieval,10.1007/s00530-006-0025-1,Multimedia Systems,Ruofei Zhang,Mingjing

A probabilistic semantic model for image annotation and multi-modal image retrieval   (Citations: 33)
BibTex | RIS | RefWorks Download
This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The con- tribution of our work is three-fold. (1) We propose a proba- bilistic semantic model in which the visual features and the textual words are connected via a hidden layer which con- stitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The associ- ation of visual features and textual words is determined in a Bayesian framework such that the confidence of the as- sociation can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collec- tion 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 gen- erative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype sys- tem 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.
Journal: Multimedia Systems - MMS , vol. 12, no. 1, pp. 27-33, 2006
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: