查询结果:   赵浩如,张永,刘国柱.基于RPN与B-CNN的细粒度图像分类算法研究[J].计算机应用与软件,2019,36(3):210 - 213,264.
中文标题
基于RPN与B-CNN的细粒度图像分类算法研究
发表栏目
图像处理与应用
摘要点击数
757
英文标题
FINE-GRANTED IMAGE CALSSIFICATION ALGORITHM BASED ON RPN AND B-CNN
作 者
赵浩如 张永 刘国柱 Zhao Haoru Zhang Yong Liu Guozhu
作者单位
青岛科技大学信息科学技术学院 山东 青岛 266000     
英文单位
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266000, Shandong, China     
关键词
细粒度分类 类间差异 双向卷积网络 非极大值抑制 特征融合
Keywords
Fine-granted classification Interclass difference B-CNN Non-maximum suppression Feature fused
基金项目
国家自然科学基金项目(61472196,61672305,61702295);山东省自然科学基金项目(ZR2014FM015)
作者资料
赵浩如,硕士生,主研领域:计算机视觉与图像工程。张永,硕士生。刘国柱,教授。 。
文章摘要
随着大数据和硬件的快速发展,细粒度分类任务应运而生,其目的是对粗粒度的大类别进行子类分类。为利用类间细微差异,提出基于RPN(Region Proposal Network)与B-CNN(Bilinear CNN)的细粒度图像分类算法。利用OHEM(Online Hard Example Mine)筛选出对识别结果影响大的图像,防止过拟合;将筛选后的图像输入到由soft-nms(Soft Non Maximum Suppression)改进的RPN网络中,得到对象级标注的图像,同时减少假阴性概率;将带有对象级标注信息的图像输入到改进后的B-CNN中,改进后的B-CNN可以融合不同层特征并加强空间联系。实验结果表明,在CUB200-2011和Standford Dogs数据集平均识别精度分别达到85.50%和90.10%。
Abstract
With the rapid development of big data and hardware, fine-grained classification has emerged. Its purpose is to classify the coarse-granted categories into subclasses. In order to use the subtle differences between similarities, we proposed a fine-granted classification algorithm based on RPN and B-CNN. The online hard example mine(OHEM) algorithm was used to screen out the images which had a great impact on the recognition results to prevent the over-fitting. Then, the selected image was input into the RPN network improved by soft non maximum suppression(soft-nms). The false negative probability was reduced, and the image with object-level annotation was obtained. The image with object-level annotation was input the improved B-CNN. The improved B-CNN could fuse features of different layers and enhanced their spatial connection. The experimental results demonstrate that the average recognition accuracy of CUB200-2011 and Stanford Dogs datasets is 85.50% and 90.10%.
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