RT Book, Section
T1 Content Based Image Retrieval by Convolutional Neural Networks
A1 Hamreras, Safa
A1 Benítez-Rochel, Rafaela
A1 Boucheham, Bachir
A1 Molina-Cabello, Miguel Ángel
A1 López-Rubio, Ezequiel
K1 Computación, Teoría de la
K1 Informática
AB In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in Content  based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic gap between low level and high-level features. Thus, improving retrieval results. Our CNN is the result of a transfer learning technique using Alexnet pretrained network. It learns how to extract representative features from a learning database and then uses this knowledge in query feature extraction. Experimentations performed on Wang (Corel 1K) database show a significant improvement in terms of precision over the state of the art classic approaches.
YR 2019
FD 2019-06-07
LK https://hdl.handle.net/10630/17778
UL https://hdl.handle.net/10630/17778
LA eng
NO Hamreras S., Benítez-Rochel R., Boucheham B., Molina-Cabello M.A., López-Rubio E. (2019) Content Based Image Retrieval by Convolutional Neural Networks. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer.
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026