Paper Info
Title
Probabilistic combination of spatial context with visual and co-occurrence information for semantic image analysis
Abstract
In this paper, a probabilistic approach to combining spatial context with visual and co-occurrence information for semantic image analysis is presented. Overall, the examined image is segmented and subsequently an initial classification of the resulting image regions to semantic concepts is performed based solely on visual information. Then, a Genetic Algorithm (GA) is introduced for deciding on the optimal semantic image interpretation, realizing image analysis as a global optimization problem. The fundamental novelty of this work is that the GA incorporates in its evolutionary procedure a set of Bayesian Networks (BNs), which probabilistically learn the impact of the available spatial, visual and co-occurrence information on the final outcome for every possible pair of semantic concepts. Experimental results on two publicly available datasets demonstrate the efficiency of the proposed approach.
Year
DOI
Venue
2010
10.1109/ICIP.2010.5652615
Image Processing
Keywords
Field
DocType
belief networks,genetic algorithms,image segmentation,semantic networks,Bayesian networks,genetic algorithm,image interpretation,image segmentation,probabilistic combination,semantic image analysis,spatial context,Spatial context,bayesian network,genetic algorithm,semantic image analysis
Semantic similarity,Data mining,Pattern recognition,Computer science,Image segmentation,Semantic network,Bayesian network,Artificial intelligence,Probabilistic latent semantic analysis,Probabilistic logic,Spatial contextual awareness,Semantic computing
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4244-7993-1
978-1-4244-7993-1
1
PageRank 
References 
Authors
0.35
5
4
Name
Order
Citations
PageRank
Georgios Th. Papadopoulos110713.01
V. Mezaris229316.26
I. Kompatsiaris328215.61
Michael G. Strintzis456854.23