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• W2126807146 abstract "We present conditional random fields (CRFs), a framework for building probabilistic models to segment and label sequence data, and use CRFs to label pixels in an image. CRFs provide a discriminative framework to incorporate spatial dependencies in an image, which is more appropriate for classification tasks as opposed to a generative framework. In this paper we apply CRF to an image classification tasks: an image labeling problem (manmade vs. natural regions in the MSRC 21-object class datasets). Parameter learning is performed using contrastive divergence (CD) algorithm to maximize an approximation to the conditional likelihood. We focus on two aspects of the classification task: feature extraction and classifiers design. We present classification results on sample images from MSRC 21-object class datasets." @default.
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• W2126807146 date "2013-09-01" @default.
• W2126807146 modified "2023-09-27" @default.
• W2126807146 title "Image Labeling Model Based on Conditional Random Fields" @default.
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• W2126807146 doi "https://doi.org/10.4028/www.scientific.net/amr.756-759.3869" @default.
• W2126807146 hasPublicationYear "2013" @default.
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