Semantic scene classification for image annotation and retrieval

Date
2008-12
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Source Title
Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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Publisher
Springer
Volume
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Pages
402 - 410
Language
English
Type
Conference Paper
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Abstract

We describe an annotation and retrieval framework that uses a semantic image representation by contextual modeling of images using occurrence probabilities of concepts and objects. First, images are segmented into regions using clustering of color features and line structures. Next, each image is modeled using the histogram of the types of its regions, and Bayesian classifiers are used to obtain the occurrence probabilities of concepts and objects using these histograms. Given the observation that a single class with the highest probability is not sufficient to model image content in an unconstrained data set with a large number of semantically overlapping classes, we use the concept/object probabilities as a new representation, and perform retrieval in the semantic space for further improvement of the categorization accuracy. Experiments on the TRECVID and Corel data sets show good performance. © 2008 Springer Berlin Heidelberg.

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Keywords
Image analysis, Image enhancement, Information theory, Pattern recognition, Probability, Random processes, Semantics, Surface plasmon resonance, Syntactics, Technical presentations, Bayesian classifiers, Color features, Contextual modeling, Data sets, Image annotations, Line structures, Model images, Occurrence probabilities, Retrieval frameworks, Semantic images, Semantic scene classifications, Semantic spaces, Trecvid, Object recognition, Line segment, Probabilistic latent semantic analysis
Citation
Published Version (Please cite this version)