부산대학교 도서관

Recently, a cellular automata learning and prediction (CALP) model was proposed that considered images as living cells and used cellular automata to evolve and synthetically augment handwritten data. In this paper, another data augmentation method is proposed inspired by the flocking pattern of birds. It is proposed that any image sample in a handwritten data set can be represented as an assembly of birds. Each bird is specifically located at a point or pixel to collectively form an image. Using a well-defined flocking mechanism, the new positions for these birds can be calculated. Each snapshot of the new position can be considered as a new version of the original image, thus generating more data. The best flocking pattern is determined using biologically-inspired genetic algorithms. The quality of the synthetic data is demonstrated by using it in a handwritten pattern recognition system. For this purpose, the CALP framework is used with few modifications. It is shown that using the new data along with the original data to train classifiers improves the accuracy of classifiers than when they are trained using only the original data. Following the same test bed as CALP, 25 different data sets and classifier pairs were used for experimentation. The experimental results show that by also using the data set generated through flocking, the performance of the base classifiers is significantly improved even when smaller training data is used. We also compare the performance of the proposed technique with other state-of-the-art oversampling methods.