Adaptive Feature and Score Level Fusion Strategy using Genetic Algorithms
| Autor: | Mohsen Ardabilian, Chokri Ben Amar, Wael Ben Soltana, Liming Chen |
|---|---|
| Přispěvatelé: | Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), REsearch Group in Intelligent Machines [Sfax] (REGIM-Lab), École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax (ENIS), SI LIRIS, Équipe gestionnaire des publications |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Image fusion
Computer science business.industry Heuristic (computer science) Feature extraction Pattern recognition 02 engineering and technology [INFO] Computer Science [cs] Machine learning computer.software_genre Facial recognition system 020202 computer hardware & architecture Statistical classification Discriminative model Feature (computer vision) Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Artificial intelligence business computer |
| Zdroj: | International Conference on Pattern recognition (ICPR) International Conference on Pattern recognition (ICPR), Aug 2010, Istanbul, Turkey. pp.4316-4319 ICPR |
| Popis: | International audience; Classifier fusion is considered as one of the best strategies for improving performances upon general purpose classification systems. On the other hand, fusion strategy space strongly depends on classifiers, features and data spaces. As the cardinality of this space is exponential, one needs to resort to a heuristic to find out a sub-optimal fusion strategy. In this work, we present a new adaptive feature and score level fusion strategy (AFSFS) based on adaptive genetic algorithm. AFSFS tunes itself between feature and matching score levels, and improves the final performance over the original on two levels, and as a fusion method, not only it contains fusion strategy to combine the most relevant features so as to achieve adequate and optimized results, but also has the extensive ability to select the most discriminative features. Experiments are provided on the FRGC database and show that the proposed method produces significantly better results than the baseline fusion methods. |
| Databáze: | OpenAIRE |
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