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• W2465345885 abstract "The success of an image classification algorithm largely depends on how it incorporates local information in the global decision. Popular approaches such as averagepooling and max-pooling are suboptimal in many situations. In this paper we propose Region Ranking SVM (RRSVM), a novel method for pooling local information from multiple regions. RRSVM exploits the correlation of local regions in an image, and it jointly learns a region evaluation function and a scheme for integrating multiple regions. Experiments on PASCAL VOC 2007, VOC 2012, and ILSVRC2014 datasets show that RRSVM outperforms the methods that use the same feature type and extract features from the same set of local regions. RRSVM achieves similar to or better than the state-of-the-art performance on all datasets." @default.
• W2465345885 created "2016-07-22" @default.
• W2465345885 creator A5038957718 @default.
• W2465345885 creator A5040218244 @default.
• W2465345885 date "2016-06-01" @default.
• W2465345885 modified "2023-09-24" @default.
• W2465345885 title "Region Ranking SVM for Image Classification" @default.
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• W2465345885 doi "https://doi.org/10.1109/cvpr.2016.326" @default.
• W2465345885 hasPublicationYear "2016" @default.
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