计算机工程2016，Vol.42Issue(12)：176-180,187,6.DOI:10.3969/j.issn.1000-3428.2016.12.031
基于HOG的目标分类特征深度学习模型
Deep Learning Model of Object Classification Feature Based on HOG
摘要
Abstract
To improve the feature extraction timeliness and classification validity of real-time classification of visual object in low computing profile,a Histogram of Oriented Gradients(HOG)-based feature deep learning model for object classification is proposed.For the requirements of high timeliness,offline deep learning strategy is applied to the classifier model to save its online training time.In view of the requirements of network depth limitation and high recognition rate,the local feature of HOG feature of an image is extracted to be used as the input of the sparse autoencoder stack so as to output the high level feature code of the sample image.The Softmax multiple classifier is designed to classify the extracted features.During the learning process of the deep neural network model,the two-stage optimization strategy is introduced,which minimizes the structural risk of every layer and fine-tune the parameters of the whole model.Using some samples of the scene image database Caltech101 and that of the handwritten digits database MNIST as the training set and the others as the test set to perform the comparative experiment,results show that the time performance of the proposed model is better than that of one-layer only Convolutional Neural Network(CNN),and the classification accuracy of the trained model is higher than that of CNN,Stacked Autoencoder(SAE)comparative models.关键词
计算机视觉/目标分类/方向梯度直方图特征/栈式自编码器/深度学习Key words
computer vision/object classification/Histogram of Oriented Gradients(HOG)feature/Stacked Autoencoder(SAE)/deep learning分类
信息技术与安全科学引用本文复制引用
何希平,张琼华,刘波..基于HOG的目标分类特征深度学习模型[J].计算机工程,2016,42(12):176-180,187,6.基金项目
重庆市教委科学技术研究计划项目(KJ1400612). （KJ1400612）
