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Performance evaluation of the combination of Compacted Dither Pattern Codes with Bhattacharyya classifier in video visual concept depiction

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dc.contributor.author Ranathunga, L
dc.contributor.author Zainuddin, R
dc.contributor.author Abdullah, NA
dc.date.accessioned 2013-10-21T02:28:51Z
dc.date.available 2013-10-21T02:28:51Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/8567
dc.description.abstract High dimensionality and multi-feature combinations can have negative effect on visual concept classification. In our research, we formulated a new compacted form which is Compacted Dither Pattern Code (CDPC) as a chromatic syntactic feature for visual feature extraction. The effectiveness of CDPC with Bhattacharyya classifier for irregular shapes based visual concepts depiction is reported in this paper. The proposed technique can reduce feature space and computational complexity while maintaining visual data mining and retrieval accuracy in high standard. Our system was empowered with Bhattacharyya classifier which has improved efficiency by considering one numeric value which is the Bhattacharyya coefficient. Experiments were conducted on various combinations and compared with different visual descriptors and classifiers. The first experiment illustrates the comparison of the CDPC based results with well known feature space reduction classes. The second and third experiments demonstrate the effectiveness of our approach with multiple perspectives of performance measures including various concepts.
dc.language en
dc.subject 3D colour histogram
dc.subject Bhattacharyya measure
dc.subject Compacted Dither Pattern Code (CDPC)
dc.subject Support Vector Machines
dc.subject Video content analysis
dc.subject Visual concept depiction
dc.title Performance evaluation of the combination of Compacted Dither Pattern Codes with Bhattacharyya classifier in video visual concept depiction
dc.type Article-Abstract
dc.identifier.year 2011
dc.identifier.journal Multimcd Tools Application
dc.identifier.issue 54
dc.identifier.volume 54
dc.identifier.pgnos 263-289


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