Publicación:
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance
Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance
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Fecha
2017-11
Autores
Ocsa, Alexander
Huillca, Jose Luis
Coronado, Ricardo
Quispe, Oscar
Arbieto, Carlos
Lopez, Cristian
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Editor
IEEE
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Abstracto
The growing volume of data and its increasing complexity require even more efficient and faster information retrieval techniques. Approximate nearest neighbor search algorithms based on hashing were proposed to query high-dimensional datasets due to its high retrieval speed and low storage cost. Recent studies promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy.
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Palabras clave
feedforward neural nets,
convolution,
data structures