重庆邮电大学学报（自然科学版）2019，Vol.31Issue(1)：37-43,7.DOI:10.3979/j.issn.1673-825X.2019.01.005
利用卷积神经网络的显著性区域预测方法
A significant regional prediction method using convolutional neural network
摘要
Abstract
There exist the problems of high cost of data acquisition and tedious handling in the significant regional prediction based on neural network. Aiming at the problem, two kinds of convolutional neural network are proposed, which are shallow convolutional neural network training from the beginning, and deep convolutional neural network with three layers from another network. Among them, the shallow network structure is simple, which can avoid overfitting problem. Deep network can make full use of the lowest model parameters, and converge faster and make better results. The proposed convolutional neural network is applied to the regression problem, and there is no linear model to train the feature map directly, but a new heap of convolution layers is trained on the migration layer. The saliency prediction is solved from the end to end frame, and the learning process evolves into a minimization problem of loss function. Testing and training are carried out on SALICON, SUN and MIT300 data set. The experimental results verify the effectiveness of the proposed method. Among them, the deep and shallow network in SALICON and SUN data is similar, the deep web in the MIT300 is better. Compared with other methods, the proposed method has good performance and is robust across data sets.关键词
显著性区域预测/卷积神经网络/损失函数/显著度图/鲁棒性Key words
significant regional prediction/convolutional neural network/loss function/saliency map/robust分类
信息技术与安全科学引用本文复制引用
李荣..利用卷积神经网络的显著性区域预测方法[J].重庆邮电大学学报（自然科学版）,2019,31(1):37-43,7.基金项目
江苏省自然科学基金 (BK20130156) （BK20130156）
无锡太湖学院校级基金 (16WUNS002) （16WUNS002）
