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• W2096514919 abstract "We present a framework that jointly learns and then uses multiple image windows for improved classification. Apart from using the entire image content as context, class-specific windows are added, as well as windows that target class pairs. The location and extent of the windows are set automatically by handling the window parameters as latent variables. This framework makes the following contributions: a) the addition of localized information through the class-specific windows improves classification, b) windows introduced for the classification of class pairs further improve the results, c) the windows and classification parameters can be effectively learnt using a discriminative max-margin approach with latent variables, and d) the same framework is suited for multiple visual tasks such as classifying objects, scenes and actions. Experiments demonstrate the aforementioned claims." @default.
• W2096514919 created "2016-06-24" @default.
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• W2096514919 date "2012-01-01" @default.
• W2096514919 modified "2023-09-27" @default.
• W2096514919 title "Classification with Global, Local and Shared Features" @default.
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• W2096514919 doi "https://doi.org/10.1007/978-3-642-32717-9_14" @default.
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