Please use this identifier to cite or link to this item: https://doi.org/10.1007/11788034_11
Title: Bayesian learning of hierarchical multinomial mixture models of concepts for automatic image annotation
Authors: Shi, R.
Chua, T.-S. 
Lee, C.-H.
Gao, S.
Issue Date: 2006
Source: Shi, R.,Chua, T.-S.,Lee, C.-H.,Gao, S. (2006). Bayesian learning of hierarchical multinomial mixture models of concepts for automatic image annotation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4071 LNCS : 102-112. ScholarBank@NUS Repository. https://doi.org/10.1007/11788034_11
Abstract: We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept structures in images. Characterizing image concepts by mixture models is one of the most effective techniques in automatic image annotation (AIA) for concept-based image retrieval. However it also poses problems when large-scale models are needed to cover the wide variations in image samples. To alleviate the potential difficulties arising in estimating too many parameters with insufficient training images, we treat the mixture model parameters as random variables characterized by a joint conjugate prior density of the mixture model parameters. This facilitates a statistical combination of the likelihood function of the available training data and the prior density of the concept parameters into a well-defined posterior density whose parameters can now be estimated via a maximum a posteriori criterion. Experimental results on the Corel image dataset with a set of 371 concepts indicate that the proposed Bayesian approach achieved a maximum F1 measure of 0.169, which outperforms many state-of-the-art AIA algorithms. © Springer-Verlag Berlin Heidelberg 2006.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41614
ISBN: 3540360182
ISSN: 03029743
DOI: 10.1007/11788034_11
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

17
checked on Dec 13, 2017

Page view(s)

74
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.