CAJ | 학술논문

针对图像的低层视觉特征和高层语义特征之间的鸿沟,利用一个多输出的BP神经网络,分析低层视觉特征,提取图像的主要颜色、灰度共生矩阵和7个不变矩向量作为网络的输入,用语义期望值作为网络的输出,并用加入动量因子和自适应学习率的BP算法来训练该网络.训练完成后,该网络能够对自然图像进行多种语义分类,从而建立起了从低层视觉特征到语义特征之间的映射.改进的BP算法提高了训练的速度和可靠性,实验证明,该方法取得了较好的检索查全率和准确率.
침대도상적저층시각특정화고층어의특정지간적홍구,이용일개다수출적BP신경망락,분석저층시각특정,제취도상적주요안색、회도공생구진화7개불변구향량작위망락적수입,용어의기망치작위망락적수출,병용가입동량인자화자괄응학습솔적BP산법래훈련해망락.훈련완성후,해망락능구대자연도상진행다충어의분류,종이건립기료종저층시각특정도어의특정지간적영사.개진적BP산법제고료훈련적속도화가고성,실험증명,해방법취득료교호적검색사전솔화준학솔.
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-occurrence 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 adap-tion,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 experi-ment proves that it has obtained the high accuracy.