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We propose a scene classification method, which combines two popular methods in the literature: Spatial Pyramid Matching (SPM) and probabilistic Latent Semantic Analysis (pLSA) modeling. The proposed scheme called Cascaded pLSA performs pLSA in a hierarchical sense after the soft-weighted BoW representation based on dense local features is extracted. We associate spatial layout information by dividing...
A correlation-enhanced similarity matching framework for medical image retrieval is presented in a local concept-based feature space. In this framework, images are presented by vectors of concepts that comprise of local color and texture patches of image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical models are built using a...
Recently, spatial pyramid matching (SPM) has achieved leading performance in scene classification. In SPM, spatial information of local features is integrated into representation by computing histograms of local features over increasingly fine regular grids on an image. But this spatial information encoding scheme is very restrictive, because it assumes that images from same scene category have similar...
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