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2013 Brazilian Conference on Intelligent Systems (BRACIS) (2013)
Fortaleza, Brazil
Oct. 19, 2013 to Oct. 24, 2013
ISBN: 978-0-7695-5092-3
pp: 131-135
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.
Feature extraction, Dictionaries, Vectors, Convolutional codes, Encoding, Image coding, Object recognition

E. F. Carvalho and P. M. Engel, "Convolutional Sparse Feature Descriptor for Object Recognition in CIFAR-10," 2013 Brazilian Conference on Intelligent Systems (BRACIS), Fortaleza, Brazil, 2014, pp. 131-135.
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