On the relevance of sparsity for image classification
نویسندگان
چکیده
منابع مشابه
On the relevance of sparsity for image classification
In this paper we empirically analyze the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experiments on two important Computer Vision problems, image categorization and pixel classification. To this end, we adopt a simple modular archite...
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The results presented in this section are encoded according to the naming convention presented in the paper, that is, by using capital letters to represent the component names. For example, OLS-SPARSEIT-ABS-MAXPCA-SVM represents the pipeline where we first extract sparse features computed by Iterative Thresholding using filters obtained with the Olshausen and Field’s algorithm, then we use the ...
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Recent years have seen an increasing interest in sparseness constraints for image classification and object recognition, probably motivated by the evidence of sparse representations internal in the primate visual cortex. It is still unclear, however, whether or not sparsity helps classification. In this paper we analyze the image classification task on CIFAR-10, a very challenging dataset, and ...
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ژورنال
عنوان ژورنال: Computer Vision and Image Understanding
سال: 2014
ISSN: 1077-3142
DOI: 10.1016/j.cviu.2014.03.009