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This paper develops a novel method to address the structural noise in samples for image classification. Recently, regression-related classification methods have shown promising results when facing the pixelwise noise. However, they become weak in coping with the structural noise due to ignoring of relationships between pixels of noise image. Meanwhile, most of them need to implement the iterative...
Domain Adaptation (DA) has attracted a lot of attention in recent years. DA aims at overcoming the covariate shift in dataset and aligning multiple existing but partially related data collections. In this paper, we propose a new DA algorithm which aligns the weighted subspaces generated from source samples and target samples. The weighted subspaces of source samples are generated using weighted Principal...
The classification of images is an important topic in several image-concerned areas. A newly proposed concept in the literature of machine learning, the Universum sample, defined as the sample that doesn't belong to any of the task-concerned classes, has been proved to be useful in training the classifiers. However, not all the Universum samples are equally helpful according to our research. In this...
Scene classification is an important application field of multimedia information technology, whereas how to extract features from image is one of the key technologies in scene classification and recognition. A new method of extracting features is presented in this paper, it extracts features through gray level-gradient co-occurrence matrix in the neighborhood of interest points, also it can reserve...
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