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- W2237379963 abstract "Abstract This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are the extraction of random subwindows described by raw pixel intensity values and the use of ensemble of extremely randomized trees to directly classify images or to learn image features. The influence of method parameters and variants is thoroughly evaluated so as to provide baselines and guidelines for future studies. Detailed results are provided on 80 publicly available datasets that depict very diverse types of images (more than 3800 image classes and over 1.5 million images)." @default.
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- W2237379963 date "2016-04-01" @default.
- W2237379963 modified "2023-09-23" @default.
- W2237379963 title "Towards generic image classification using tree-based learning: An extensive empirical study" @default.
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- W2237379963 doi "https://doi.org/10.1016/j.patrec.2016.01.006" @default.
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