Dimensionality Reduction for Content-Based Image Classification
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
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ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
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Effective Feature Space Reduction with Imbalanced Data for Semantic Concept Detection
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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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.