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Scene classification is an extremely challenging task owing to the complexity of the scene content. In this paper, a novel method is designed to harvest the discriminative representation for the scene classification. The proposed model simultaneously takes both discriminative patches and entire scene image into consideration. First, the discriminative patches are extracted from the raw scene image...
This paper aims to address the problem of unsupervised scene model adaptation for transferring semantic labels from ground level images to aerial and satellite scenes. Specifically, we present a novel unsupervised domain adaptation algorithm based on subspace alignment. The core idea is to reduce the feature distribution discrepancy between ground view images and overhead view remote sensing images...
We address the problem of unsupervised visual domain adaptation for transferring scene category models and scene attribute models from ground view images to overhead view very high-resolution (VHR) remote sensing images. We introduce a discriminative cross-view subspace alignment algorithm where each view is represented by a subspace spanned by eigenvectors. The source subspace is created using partial...
This article att1empted to build a network based classroom for users to communicate with each other and edit collaboratively based on the CSCW concept. It proposed a web based “Online Collaborative Editor System” which based on CSCW to establish a visualized collaborative learning group to prompt the discussion, communication and experience sharing between teachers and students, students and students...
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