RT Journal Article
T1 Content-based image retrieval by ensembles of deep learning object classifiers.
A1 Hamreras, Safa
A1 Boucheham, Bachir
A1 Molina-Cabello, Miguel Ángel
A1 Benítez-Rochel, Rafaela
A1 López-Rubio, Ezequiel
K1 Redes neuronales (Informática)
K1 Aprendizaje automático (Inteligencia artificial)
K1 Imágenes - Recuperación
AB Ensemble learning has demonstrated its efficiency in many computer vision tasks. In this paper, we address this paradigm within content based image retrieval (CBIR). We propose to build an ensemble of convolutional neural networks (CNNs), either by training the CNNs on different bags of images, or by using CNNs trained on the same dataset, but having different architectures. Each network is used to extract the class probability vectors from images to use them as representations. The final image representation is then generated by combining the extracted class probability vectors from the built ensemble. We show that the use of CNN ensembles is very efficient in generating a powerful image representation compared to individual CNNs. Moreover, we propose an Averarge Query Expansion technique for our proposal to enhance the retrieval results. Several experiments were conducted to extensively evaluate the application of ensemble learning in CBIR. Results in terms of precision, recall, and mean average precision show the outperformance of our proposal compared to the state of the art.
YR 2020
FD 2020-05-20
LK https://hdl.handle.net/10630/29702
UL https://hdl.handle.net/10630/29702
LA eng
NO Hamreras, S., Boucheham, B., Molina-Cabello, M. A., Benítez-Rochel, R., & López-Rubio, E. (2020). Content based image retrieval by ensembles of deep learning object classifiers. Integrated Computer-Aided Engineering, 27(3), 317–331. https://doi.org/10.3233/ICA-200625
NO Copyright Owner. Versión definitiva disponible en el DOI indicado.Hamreras, S., Boucheham, B., Molina-Cabello, M. A., Benitez-Rochel, R., & Lopez-Rubio, E. (2020). Content based image retrieval by ensembles of deep learning object classifiers. Integrated computer-aided engineering, 27(3), 317-331.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026