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Noname manuscript no. (will be inserted by the editor) unsupervised high-level feature learning by ensemble projection for semi-supervised image classification and image clustering... Experiments on eight standard datasets show that: (1) EP outperforms previous methods for semi-supervised image classification; (2) EP produces promising results for self-taught image classification, where unlabeled samples are a random collection of images rather than ...
général - core.ac.uk - PDF: www.vision.ee.ethz.ch
Isprs igu cig table of contents authors index search exit sipt uci acsg table des matires index des auteurs recherches sortirSupervised image classification is based on assembling statistics between site-specific ground observations and remotely sensed measurements....
Parallelizing maximum likelihood classification (mlc) for supervised image classification by pipelined thread approach through high-level synthesis (hls) on fpga cluster... This article introduced how to conduct supervised image classification by implementing maximum likelihood classification (MLC) over big image data on a field programmable gate array (FPGA) cloud....
 PDF: dx.doi.org
Experiments with multi-view multi-instance learning for supervised image classificationAbstract—In this paper we empirically investigate the benefits of multi-view multi-instance (MVMI) learning for supervised image classification....
Experiments with multi-view multi-instance learning for supervised image classificationIn this paper we empirically investigate the benefits of multi-view multi-instance (MVMI) learning for supervised image classification....
 PDF: core.ac.uk

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