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Semantic Scene Classification for Image Annotation and Retrieval

Summary: Semantic Scene Classification for Image
Annotation and Retrieval
¨Ozge C¸avu¸s and Selim Aksoy
Department of Computer Engineering, Bilkent University, Ankara, 06800, Turkey
Abstract. We describe an annotation and retrieval framework that uses
a semantic image representation by contextual modeling of images us-
ing occurrence probabilities of concepts and objects. First, images are
segmented into regions using clustering of color features and line struc-
tures. Next, each image is modeled using the histogram of the types of its
regions, and Bayesian classifiers are used to obtain the occurrence proba-
bilities of concepts and objects using these histograms. Given the obser-
vation 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.
1 Introduction
Image annotation and content-based retrieval have been very active research


Source: Aksoy, Selim - Department of Computer Engineering, Bilkent University


Collections: Computer Technologies and Information Sciences