Name
Abstract | ||
|---|---|---|
In this paper, we present a new framework for Content Based Image Retrieval (CBIR), based on Dynamic Ensemble Selection (DES) of classifiers. Herein, the classifiers consist of Convolutional Neural Networks (CNNs) that output the class probability vector of each input image. First, a diverse ensemble is built by training several weak classifiers on different training subsets, from the retrieval database. Then, each training image is passed throughout all the candidate classifiers to extract its class probability vector. These extracted vectors are then combined to make its final representation. When a new query image is input, the level of competence of all the candidate classifiers is measured on the region of competence of this image, in the training set. Then, only the most competent classifiers are selected to extract the class probability vectors from each query image. They are then combined to make a more discriminant image representation. To the best of our knowledge DES has never been applied to the CBIR area, which is the novelty of our research. The obtained results demonstrate the effectiveness of the dynamic selection, compared to the use of all the ensemble members, in terms of precision, recall, and mean average precision. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9533856 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
Content Based Image Retrieval, Ensemble Learning, Dynamic Ensemble Selection, Convolutional Neural Networks | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Safa Hamreras | 1 | 0 | 0.34 |
| Bachir Boucheham | 2 | 39 | 8.43 |
| Miguel A. Molina-Cabello | 3 | 1 | 6.44 |
| R. Benítez-Rochel | 4 | 2 | 1.42 |
| Ezequiel López-Rubio | 5 | 21 | 5.48 |