Semantic scene classification for image annotation and retrieval
Author
Çavuş, Özge
Aksoy, Selim
Date
2008-12Source Title
Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Publisher
Springer
Pages
402 - 410
Language
English
Type
Conference PaperItem Usage Stats
146
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102
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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.
Keywords
Image analysisImage 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
Permalink
http://hdl.handle.net/11693/26791Published Version (Please cite this version)
http://dx.doi.org/10.1007/978-3-540-89689-0_44Collections
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