NDLI: A probabilistic semantic model for image annotation and multi-modal image retrieval

Content Provider	Springer Nature Link
Author	Zhang, Ruofei Zhang, Zhongfei Mark Li, Mingjing Ma, Wei Ying Zhang, Hong Jiang
Copyright Year	2006
Abstract	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 7736 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.
Starting Page	27
Ending Page	33
Page Count	7
File Format	PDF
ISSN	09424962
Journal	Multimedia Systems
Volume Number	12
Issue Number	1
e-ISSN	14321882
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-04-27
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image annotation Multi-modal image retrieval Probabilistic semantic model Evaluation Computer Graphics Multimedia Information Systems Data Encryption Data Storage Representation Operating Systems Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Media Technology Hardware and Architecture Software

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