A Feature Subset Based Decision Fusion Approach for Scene Classification Using Color, Spectral, and Texture Statistics

Content Based Image Retrieval (CBIR) deals withthe automatic extraction of images from a database based ona query. For efficient retrieval the digital image CBIR requiressupport of scene classification algorithms. The Cognitive psychology suggests that the basic level classification is efficient withthe global features. However, a detailed classification requires acombination of the global and the local features. In this paper, we propose a decision fusion of the classification results based onlocal and global features. The proposed algorithm is a multi stageapproach, in the stage-1 the algorithm separates the completedatabase into natural and artificial images using spectral features. In the stage-2, the texture and color features are used to furtherclassify the image database into subcategories. The results of theproposed decision fusion algorithm give a 5% better classificationaccuracy than the single best classifier.