Content based image retrieval by convolutional neural networks

• Autores: Safa Hamreras, Rafaela Benitez Rochel, Bachir Boucheham, Miguel Ángel Molina Cabello, Ezequiel López Rubio
• Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 277-286
• Idioma: inglés
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• Resumen

  • 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 lowlevel 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.