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. The MATLAB code of our system will be publicly available at http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=. Our free MATLAB toolbox can be used to verify the results of our system. We also hope that our toolbox will serve as the foundation for further explorations by other researchers in the computer vision field.

Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system

LUMINI, ALESSANDRA;
2014

Abstract

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. The MATLAB code of our system will be publicly available at http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=. Our free MATLAB toolbox can be used to verify the results of our system. We also hope that our toolbox will serve as the foundation for further explorations by other researchers in the computer vision field.
MAGALLAT GAMIAT AL-MALIK SAʹUD. AL-ʹULUM
Loris Nanni;Alessandra Lumini;Sheryl Brahnam
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/474570
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