Traditional image set classification methods measure the similarities between different sets based on the extracted characteristics of each set.
Traditional image set classification methods measure the similarities between different sets based on the extracted characteristics of each set.
Traditional image set classification methods measure the similarities between different sets based on the extracted characteristics of each set.
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What is sparse representation classification?
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Multi-task image set classification via joint representation with class-level sparsity and intra-task low-rankness · Query set centered sparse projection ...
seeks a sparse coding for the incoming image ... The gray-level image and LBP image from each test ... set containing images of 101 categories of objects as well.
In this paper, we proposed superpixel-based nonlocal weighting joint sparse representation classification (SNLW-JSRC) for hyperspectral image classification.
Abstract. Traditional sparse representation algorithms usually operate in a single Euclidean space. This paper leverages a self-explanatory re-.
A set-level joint sparse representation for image set classification. Peng ... Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces.
Video-based Face Recognition (VFR) can be converted into the problem of measuring the similarity of two image sets, where the examples from a video clip ...
Aug 10, 2020 · An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper.