Deep Hashing Based on Class-Discriminated Neighborhood Embedding
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Otros documentos de la autoría: kang, jian; Fernandez-Beltran, Ruben; Zhen, Ye; Tong, Xiaohua
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Título
Deep Hashing Based on Class-Discriminated Neighborhood EmbeddingFecha de publicación
2020-09-30Editor
Institute of Electrical and Electronics EngineersISSN
1939-1404Cita bibliográfica
KANG, Jian, et al. Deep Hashing Based on Class-Discriminated Neighborhood Embedding. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, vol. 13, p. 5998-6007.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS)
owing to their prominent capabilities for capturing the semantics
from complex RS scenes and generating ... [+]
Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS)
owing to their prominent capabilities for capturing the semantics
from complex RS scenes and generating the associated hash codes
in an end-to-end manner. Most existing deep-hashing methods
exploit pairwise and triplet losses to learn the hash codes with
the preservation of semantic-similarities which require the construction of image pairs and triplets based on supervised information (e.g., class labels). However, the learned Hamming spaces
based on these losses may not be optimal due to an insufficient
sampling of image pairs and triplets for scalable RS archives. To
solve this limitation, we propose a new deep-hashing technique
based on the class-discriminated neighborhood embedding, which
can properly capture the locality structures among the RS scenes
and distinguish images class-wisely in the Hamming space. An
extensive experimentation has been conducted in order to validate
the effectiveness of the proposed method by comparing it with
several state-of-the-art conventional and deep-hashing methods.
The related codes of this article will be made publicly available for
reproducible research by the community. [-]
Publicado en
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 13, 2020Proyecto de investigación
2018YFB0505400, 2017YFB0502700, National Natural Science Foundation of China/41631178, Ministry of Science, Innovation, and Universities of Spain/RTI2018-098651-B-C54, Valencian Government of Spain/GV/2020/167, FEDER-Junta de Extremadura/Ref. GR18060, European Union under the H2020 EOXPOSURE/734541Derechos de acceso
info:eu-repo/semantics/openAccess
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