QQ客服 官方微博 反馈留言

	一种基于最优路径搜索的图像分类方法
	陈洁萍 ; 甘泉 ; 张慧 ; CHEN Jieping ; GAN Quan ; ZHANG Hui
	2016-03-30 ; 2016-03-30
关键词	图像分类 分支界定 最优路径 贪婪算法 效率 精度 ocean circulation model parallel I/O optimization ADIOS TP391.41
其他题名	Image Classification Algorithm Based on Optimal Path Search
中文摘要	目前已经有多种基于树的算法来解决多类别图像分类问题,然而由于选择的学习和贪婪预测策略不当,这些算法在分类精度和测试时间效率间不能实现很好的均衡。提出一种新的分类器,当树形架构已知时能在效率和精度间实现很好的折中。首先,将图像分类问题转化为树结构中最优路径的搜索问题,提出新的类似于分支界定的算法来实现最优路径的高效搜索。其次,使用结构化支持向量机(SSVM)在多种边界约束下联合训练分类器。仿真实验结果表明,相对于当前最新"基于树"的贪婪算法,当应用于Caltech-256、SUN和Image Net 1K等数据集时,该算法在效率较高时的精度分别上升了4.65%,5.43%和4.07%。; Many algorithms based on tree are proposed to solve the image classification problem for a large number of categories. Due to learning and greedy prediction strategy choice of undeserved,methods based on tree-based representations cannot achieve good trade-off between accuracy and test time efficiency. In this paper,a classifier is proposed which achieves a better trade-off between efficiency and accuracy with a given tree-shaped hierarchy. Firstly,the image classification problem is converted as finding the best path in the tree hierarchy,and a novel branch and bound-like algorithm is introduced to efficiently search for the best path. Secondly,the classifiers are trained using a Structured SVM( SSVM) formulation with various bound constraints. Simulation results show that,this method achieves a significant 4. 65%,5. 43%,and 4. 07% improvement in accuracy at high efficiency compared to state-of-the-art greedy"tree-based"methods on Caltech-256,SUN and Image Net 1K dataset,respectively.
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://ir.lib.tsinghua.edu.cn/ir/item.do?handle=123456789/147007]
专题	清华大学
推荐引用方式 GB/T 7714	陈洁萍,甘泉,张慧,等. 一种基于最优路径搜索的图像分类方法[J],2016, 2016.
APA	陈洁萍,甘泉,张慧,CHEN Jieping,GAN Quan,&ZHANG Hui.(2016).一种基于最优路径搜索的图像分类方法..
MLA	陈洁萍,et al."一种基于最优路径搜索的图像分类方法".(2016).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。