主页期刊介绍编委会编辑部服务介绍道德声明在线审稿编委办公English
2020-2021年专刊出版计划 微信服务介绍 最新一期:2020年第10期
     
在线出版
各期目录
纸质出版
分辑系列
论文检索
论文排行
综述文章
专刊文章
美文分享
各期封面
E-mail Alerts
RSS
旧版入口
中国科学院软件研究所
  
投稿指南 问题解答 下载区 收费标准 在线投稿
邵健,赵师聪.基于异构特征组效应的图像人物和动作标注方法.软件学报,2010,21(zk):205-213
基于异构特征组效应的图像人物和动作标注方法
An Approach for Human and Motion Word Annotation with the Grouping Effect of Heterogeneous Features
投稿时间:2010-07-20  修订日期:2010-11-03
DOI:
中文关键词:  Group LASSO  生成模型  组效应动词标注
英文关键词:Group LASSO  generative model  group effect  motion word annotation
基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60833006, 61070068 (国家自然科学基金); the China Postdoctoral Science Foundation under Grant No.20090451448; the National Key Technology R&D Program of China under Grant No.2007BAH11B05; the Fundamental Research Funds for the Central Universities of China under Grant No.KYJD09008
作者单位E-mail
邵健 浙江大学 计算机科学与技术学院,浙江 杭州 310027 jshao@zju.edu.cn 
赵师聪 浙江大学 计算机科学与技术学院,浙江 杭州 310027  
摘要点击次数: 2840
全文下载次数: 3476
中文摘要:
      从图像伴随文本中选择合适动词去描述图像中人物动作对于理解图像语义具有重要意义.现有方法通常学习得到表示图像人物和运动与其标注名词-动词之间概率的生成模型,然后使用这一得到的生成模型对训练集以外图像中人物运动进行识别.但是,这一方法忽略了图像中高维异构特征之间固有存在的组效应.实际上,不同类型异构特征在图像语义理解过程中具有不同区别性,例如手臂特征对人挥手这一动作最具有区别性.为了识别图像中人物运动进而对其进行标注,提出了通过Group LASSO 从高维异构姿势特征中选择最具区别性特征,最终学习得到生成模型的方法.实验结果表明,该方法对姿态变化较大动作进行识别时取得了更好结果.
英文摘要:
      It is very important to select the most suitable motion words from surrounding text to describe the persons’ motion expressed in images during semantic understanding. Traditional approaches often learn a generative model to denote the occurrence probability between visual objects & motion and their corresponding annotated tags, and the learned model is then utilized to recognize persons’ actions in a new image outside training samples. However, all of existing approaches neglect the grouping effect of high-dimensional heterogeneous features inherent in images. In fact, different kinds of heterogeneous features have different intrinsic discriminative power for image understanding. For instance, the features extracted from arms are most discriminative to human waving motion. The selection of groups of discriminative features for motion recognition is hence crucial. In this paper, we propose an approach to select discriminative subgroup visual features from high-dimensional pose features by Group LASSO during the learning of generative model in order to boost the motion recognition. Experiments show that the proposed approach in this paper can obtain better performance for the recognition of motions with large pose change.
HTML  下载PDF全文  查看/发表评论  下载PDF阅读器
 

京公网安备 11040202500064号

主办单位:中国科学院软件研究所 中国计算机学会 京ICP备05046678号-4
编辑部电话:+86-10-62562563 E-mail: jos@iscas.ac.cn
Copyright 中国科学院软件研究所《软件学报》版权所有 All Rights Reserved
本刊全文数据库版权所有,未经许可,不得转载,本刊保留追究法律责任的权利