应用深度极限学习机的立体图像质量评价方法
发布时间：2018-10-10 12:46
【摘要】：极限学习机(Extreme Learning Machine,ELM)较其它神经网络具有训练速度快、泛化能力强的特点.然而对于高维的立体图像数据,无论ELM还是传统神经网络均需经过特征提取的预处理,但是传统特征提取的方式是否真正符合人的感知特性有待进一步研究.深度学习是一种模拟人脑深层次学习的神经网络,因此提出基于深度结构的极限学习机算法(Deep Extreme Learning M achine,D-ELM),该方法通过深度学习预训练来逐层表达输入数据的分布式特征,从而实现原始数据的特征提取.实验结果表明,深度结构下的ELM网络更加稳定高效,对于250幅不同等级的立体图像样本进行测试后的准确率达到了96.11%.此外,本文还分析了隐节点数对网络的影响,而且将D-ELM与ELM、支持向量机等在立体图像质量评价上的性能进行了比较.
[Abstract]:Extreme Learning Machine (Extreme Learning Machine,ELM) has the advantages of faster training speed and better generalization ability than other neural networks. However, for high-dimensional stereo image data, both ELM and traditional neural networks need to be preprocessed by feature extraction, but whether the traditional feature extraction method really accords with human perception needs further study. Deep learning is a neural network that simulates the deep learning of human brain. Therefore, an algorithm of extreme learning machine (Deep Extreme Learning M achine,D-ELM) based on depth structure is proposed, which can express the distributed feature of input data layer by layer through the pre-training of depth learning. In order to achieve the feature extraction of the original data. The experimental results show that the ELM network with depth structure is more stable and efficient, and the accuracy of testing 250 stereo images of different grades is 96.11. In addition, the influence of the number of hidden nodes on the network is analyzed, and the performance of D-ELM and ELM, support vector machine in stereo image quality evaluation is compared.
【作者单位】： 天津大学电气自动化与信息工程学院;
【基金】：国家自然科学基金项目(61002028)资助 国家“八六三”计划项目(2012AA011505,2012AA03A301)资助
【分类号】：TP18;TP391.41
[Abstract]:Extreme Learning Machine (Extreme Learning Machine,ELM) has the advantages of faster training speed and better generalization ability than other neural networks. However, for high-dimensional stereo image data, both ELM and traditional neural networks need to be preprocessed by feature extraction, but whether the traditional feature extraction method really accords with human perception needs further study. Deep learning is a neural network that simulates the deep learning of human brain. Therefore, an algorithm of extreme learning machine (Deep Extreme Learning M achine,D-ELM) based on depth structure is proposed, which can express the distributed feature of input data layer by layer through the pre-training of depth learning. In order to achieve the feature extraction of the original data. The experimental results show that the ELM network with depth structure is more stable and efficient, and the accuracy of testing 250 stereo images of different grades is 96.11. In addition, the influence of the number of hidden nodes on the network is analyzed, and the performance of D-ELM and ELM, support vector machine in stereo image quality evaluation is compared.
【作者单位】： 天津大学电气自动化与信息工程学院;
【基金】：国家自然科学基金项目(61002028)资助 国家“八六三”计划项目(2012AA011505,2012AA03A301)资助
【分类号】：TP18;TP391.41
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