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oai:riuma.uma.es:10630/177782026-02-03T11:38:44Zcom_10630_2254col_10630_37956

Content Based Image Retrieval by Convolutional Neural Networks Hamreras, Safa Benítez-Rochel, Rafaela Boucheham, Bachir Molina-Cabello, Miguel Ángel López-Rubio, Ezequiel Computación, Teoría de la Informática 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. 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. 2019-06-07T08:15:07Z 2019-06-07T08:15:07Z 2019 2019-06-07 book part https://hdl.handle.net/10630/17778 eng 8th International Work-Conference on the Interplay between Natural and Artificial Computation Almería, España Junio de 2019 open access