Parallelization Design of Image Semantic Classification Algorithm Under MPI Cluster Environment
To narrow the gap between image low-level visual features and high-level semantic features,BP neural networks are often used to carry out the natural images semantic classification for its fine fitting ability. Aiming at the problems of long learning time and low efficiency classification in the process of single BP neural network,specifically with large-scale image data and complex classification mode. The paper establishes a multi-BP neural network parallel integration model of image semantic classification,through designing mixed master-slave and peer-to-peer parallel structure algorithm under the environment of MPI cluster,training and classification images by class group. The experimental results achieved higher recall rate and the accuracy,the approximately linear speed-up ratio verify the correctness and high efficiency of the parallel algorithm.