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Convolutional Sparse Feature Descriptor for Object Recognition in CIFAR-10

Edigleison Francelino CarvalhoPaulo Martins Engel

Seleção de Gateways Móveis em Redes Heterogêneas Sem Fio com Múltiplos Saltos

Fabrício LobatoOtávio Rodrigues Jr.Kelvin Lopes Dias

In this work we address the problem of feature extraction for image object recognition. We propose a new, learned, feature descriptor for images, the convolutional sparse descriptor, which is based on recent advances in machine learning. It computes a spatial representation of the entire input image based on feature responses of local descriptors. The feature responses are calculated using a learned dictionary, which is learned using the sparse coding algorithm, instead of the vector quantization (VQ). Experiments on the benchmark CIFAR-10 show that our method outperforms several state-of-the-art algorithms.

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6726438&punumber%3D6723866%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6726406%29%26pageNumber%3D2

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