A massive images classification method based on MapReduce parallel fuzzy C-means clustering

Abstract

Aiming at the low performance of classifying images under the computing model of single node. With GLCM (Gray Level Co-occurrence Matrix) which fuses gray level with texture of image, a parallel fuzzy C-means clustering method based on MapReduce is designed to classify massive images and improve the real-time performance of classification. The experimental results show that the speedup ratio of this method is more than 10% higher than that of the other two methods, moreover, the accuracy of image classification has not decreased. It shows that this method has high real-time processing efficiency in massive images classification.