Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system
In the last few years, several ensemble approaches have been proposed for building high performance systems for computer vision. In this paper we propose a system that incorporates several perturbation approaches and descriptors for a generic computer vision system. Some of the approaches we investigate include using different global and bag-of-feature-based descriptors, different clusterings for codebook creations, and different subspace projections for reducing the dimensionality of the descriptors extracted from each region. The basic classifier used in our ensembles is the Support Vector Machine. The ensemble decisions are combined by sum rule. The robustness of our generic system is tested across several domains using popular benchmark datasets in object classification, scene recognition, and building recognition. Of particular interest are tests using the new VOC2012 database where we obtain an average precision of 88.7 (we submitted a simplified version of our system to the person classification-object contest to compare our approach with the true state-of-the-art in 2012). Our experimental section shows that we have succeeded in obtaining our goal of a high performing generic object classification system.
Nanni, Loris, Alessandra Lumini, and Sheryl Brahnam. Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system." Journal of King Saud University-Science 26, no. 2 (2014): 89-100."
DOI for the article
Management and Information Systems