Research on Massive Image Classification Method Based on Parallel Hybrid Classifier Algorithm

With the advent of the era of big data, various types of image data have exploded. Traditional image classification algorithms have the problems of low efficiency and low accuracy, and cannot meet the processing requirements of massive image data. Aiming at the above problems, this paper proposes a massive image classification method based on parallel hybrid classifier algorithm. This algorithm combines the Adaboost algorithm with the RBF algorithm, combines multiple RBF classifiers into a strong classifier, and uses the MapReduce parallel programming model to parallelize the Adaboost-RBF algorithm. The performance test of the algorithm was performed. using the Caltech256 data set. The test results show that compared with the ordinary Adaboost-RBF algorithm, the parallel hybrid classifier algorithm takes less time to run and the average classification accuracy is increased by 26%. This algorithm can meet the needs of automatic classification of massive images.