关键词: 语义鸿沟, BP神经网络, 多输出, 改进的BP算法, 图像分类

Abstract: This paper establishes a multi-output BP neural network.It aims at to overcome the considerable gap between image low-level features and high-level semantic features.This method analyzes low-level features and extractes the images main color，gray level co-occurvence matrix and monent invariant vector.Use the vector as the network’s imput and the expections as its output，the system trains the network with improved BP algorithm.The arithmetic joines the monentun factor and learning rate adaption，when the training is over，this network can classify natural images.So it has established the mapping between the low-level features and high-level semantic features.In addition，the improved arithmetic has increased the rate and the stability.The experiment proves that it has obtained the high accuracy.

Key words: semantic gap, BP neural network, multi-output, improved BP algorithm, image classification