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

Highlights：

• In the DEL-BoW modeling technique, two limits are estimated to the random variable of the performance in order to guarantee ergodicity of the process.

• The first limit, with a larger radius of convergence, allows to have robustness against random initialization in the modeling process and helps in the estimation of optimal model-order.

• The second limit, with a smaller radius of convergence, helps in achieving enhanced modeling performance for the optimal model-order.

• The DEL-BoW modeling technique is applied to Caltech-101, Caltech-256, 15-Scenes, and Flower-102 datasets.

• Excellent modeling performance is reported when using DEL-BoW modeling technique to the aforementioned datasets.

摘要

•A dual ergodicity limits-based bag-of-words (DEL-BoW) technique is suggested that guarantees robustness against random initialization, estimates optimal model-order, and achieves enhanced modeling performance.•In the DEL-BoW modeling technique, two limits are estimated to the random variable of the performance in order to guarantee ergodicity of the process.•The first limit, with a larger radius of convergence, allows to have robustness against random initialization in the modeling process and helps in the estimation of optimal model-order.•The second limit, with a smaller radius of convergence, helps in achieving enhanced modeling performance for the optimal model-order.•The DEL-BoW modeling technique is applied to Caltech-101, Caltech-256, 15-Scenes, and Flower-102 datasets.•Excellent modeling performance is reported when using DEL-BoW modeling technique to the aforementioned datasets.