Approximate nearest neighbors by deep hashing on large-scale search: Comparison of representations and retrieval performance

• Autores: Ocsa, Alexander, Huillca, Jose Luis, Coronado, Ricardo, Quispe, Oscar, Arbieto, Carlos, Lopez, Cristian
• Formato: objeto de conferencia
• Fecha de Publicación: 2017
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/1273
• Enlace del recurso: https://hdl.handle.net/20.500.12390/1273; https://doi.org/10.1109/la-cci.2017.8285730
• Nivel de acceso: acceso abierto
• Materia: feedforward neural nets; convolution; data structures; https://purl.org/pe-repo/ocde/ford#5.08.02

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.