Journal
PATTERN RECOGNITION
Volume 99, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2019.107094
Keywords
Bag-of-words; Ergodicity; Statistical modeling; Stochastic process
Funding
- National Science Foundation [DRK-12 DRL: 1417769, ITEST DRL: 1614085, RET Site EEC: 1542286]
- NY Space Grant Consortium grant [76156-10488]
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This paper suggests an efficient dual ergodicity limits-based bag-of-words (DEL-BoW) modeling technique. The suggested DEL-BoW technique estimates two limits of ergodicity of a discrete random variable (drv) that is formed from the BoW classification performance of multiple runs. The first limit of ergodicity is estimated with a relatively larger ball of convergence to keep the drv shorter. Hence both robustness against random initialization and estimation of the optimal model-order are realized with a reduced number of iterations. Once the optimal model-order is estimated, the radius of ball of convergence is reduced and a second limit of ergodicity is estimated. Reducing the ball of convergence enlarges the size of the considered performance drv that enhances the classification performance. Experiments conducted on Caltech-101, Caltech-256, 15-Scenes, and Flower-102 datasets resulted in classification accuracy of 86.91%, 72.57%, 90.57%, and 90.86%, respectively. Comparison with state-of-the-art techniques shows the excellent performance of the DEL-BoW modeling process. (C) 2019 Elsevier Ltd. All rights reserved.
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