Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-19651-6_27 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Content based image retrieval,Convolutional neural networks,Feature extraction | Convolutional neural network,Computer science,Transfer of learning,Semantic gap,Image processing,Feature extraction,Artificial intelligence,Content based retrieval,Machine learning,Content-based image retrieval | Conference |
Volume | ISSN | Citations |
11487 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Safa Hamreras | 1 | 0 | 0.34 |
| Rafaela Benítez-Rochel | 2 | 1 | 0.68 |
| Bachir Boucheham | 3 | 39 | 8.43 |
| Miguel A. Molina-Cabello | 4 | 1 | 6.44 |
| Ezequiel López-Rubio | 5 | 323 | 39.73 |