par Tran, Duc Toan;Jansen, Bart;Deklerck, Rudi;Debeir, Olivier 
Référence Proceedings of SPIE - The International Society for Optical Engineering, 9445, 94450Y
Publication Publié, 2015
Référence Proceedings of SPIE - The International Society for Optical Engineering, 9445, 94450Y
Publication Publié, 2015
Article révisé par les pairs
| Résumé : | Recently, efficient image descriptors have shown promise for image classification tasks. Moreover, methods based on the combination of multiple image features provide better performance compared to methods based on a single feature. This work presents a simple and efficient approach for combining multiple image descriptors. We first employ a Naive-Bayes Nearest-Neighbor scheme to evaluate four widely used descriptors. For all features, a Image-to-Classa distances are directly computed without descriptor quantization. Since distances measured by different metrics can be of different nature and they may not be on the same numerical scale, a normalization step is essential to transform these distances into a common domain prior to combining them. Our experiments conducted on a challenging database indicate that z-score normalization followed by a simple sum of distances fusion technique can significantly improve the performance compared to applications in which individual features are used. It was also observed that our experimental results on the Caltech 101 dataset outperform other previous results. |
