Abstract
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.
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Acknowledgments
This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2014-53465-R, project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Andalusia (Spain) under projects TIC-6213, project name Development of Self-Organizing Neural Networks for Information Technologies; and TIC-657, project name Self-organizing systems and robust estimators for video surveillance. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPU. Authors are also immensely grateful for ERASMUS+ program, CEI.MAR (Campus de Excelencia International del Mar), and University of 20 August 1955 for making this collaborative work possible.
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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, Cham. https://doi.org/10.1007/978-3-030-19651-6_27
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DOI: https://doi.org/10.1007/978-3-030-19651-6_27
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