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A Statistical Learning Approach to Spatial Context Exploitation for Semantic Image Analysis

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A Statistical Learning Approach to Spatial Context Exploitation for Semantic Image Analysis
In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented. The proposed method constitutes an extension of the key parts of the authors’ previous work on spatial context utilization, where a Genetic Algorithm (GA) was introduced for exploiting fuzzy directional relations after performing an initial classification of image regions to semantic concepts using solely visual information. In the extensions reported in this work, a more elaborate approach is followed during the spatial knowledge acquisition and modeling process. Additionally, the impact of every resulting spatial constraint on the final outcome is adaptively adjusted. Experimental results as well as comparative evaluation on three datasets of varying complexity in terms of the total number of supported semantic concepts demonstrate the efficiency of the proposed method.
Georgios Th. Papadopoulos, Vasileios Mezaris, Ioan
Added 07 Dec 2010
Updated 07 Dec 2010
Type Conference
Year 2010
Where ICPR
Authors Georgios Th. Papadopoulos, Vasileios Mezaris, Ioannis Kompatsiaris, Michael G. Strintzis
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