摘要
图像分类是根据图像的信息将不同类别的图像区分开来,是计算机视觉中重要的基本问题,也是图像检测、图像分割、物体跟踪、行为分析等其他高层视觉任务的基础。深度学习是机器学习研究中的一个新的领域,其动机在于建立、模拟人脑进行分析学习的神经网络,它模仿人脑的机制来解释数据,例如图像、声音和文本。该系统基于Caffe深度学习框架,首先对数据集进行训练分析构建深度学习网络,提取数据集图像特征信息,得到数据对应的分类模型,然后以bvlc-imagenet训练集模型为基础,对目标图像进行扩展应用,实现“以图搜图”Web应用。
Image classification is to distinguish different types of images based on image information.It is an important basic issue in computer vision,and is also the fundamental for image detection,image segmentation,object tracking and behavior analysis.Deep learning is a new field in machine learning research.Its motivation is to simulate the neural network of the human brain for analytical learning.Like the human brain,deep learning can interpret the data of images,sounds and texts.The system is based on the Caffe deep learning framework.Firstly,the data set is trained and analyzed,and a model based on deep learning network is built to obtain the image feature information and corresponding data classification.Then the target image is expanded based on the bvlc-imagenet training set model.And finally,"search an image with an image"Web application is achieved.
作者
张璘
杨丰墒
Zhang Lin;Yang Fengshang(School of Opto-Electronic and Communication Engineering,Xiamen University of Technology,Xiamen 361024,China)
出处
《电子技术应用》
2019年第12期51-55,共5页
Application of Electronic Technique
关键词
图像分类
深度学习
Caffe框架
卷积神经网络
image classification
depth learning
Caffe framework
convolution neural network
