Estimation of ergodicity limits of bag-of-words modeling for guaranteed stochastic convergence

Abstract

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.