Learning latent semantic model with visual consistency for image analysis
Cheng, Jian1; Li, Peng1; Rui, Ting2; Lu, Hanqing1
2015-02-01
发表期刊MULTIMEDIA TOOLS AND APPLICATIONS
卷号74期号:4页码:1341-1356
文章类型Article
摘要Latent semantic models (e.g. PLSA and LDA) have been successfully used in document analysis. In recent years, many of the latent semantic models have also been proved to be promising for visual content analysis tasks, such as image clustering and classification. The topics and words which are two of the key components in latent semantic models have explicit semantic meaning in document analysis. However, these topics and words are difficult to be described or represented in visual content analysis tasks, which usually leads to failure in practice. In this paper, we consider simultaneously the topic consistency and word consistency in semantic space to adapt the traditional PLSA model to the visual content analysis tasks. In our model, the a"" (1)-graph is constructed to model the local neighborhood structure of images in feature space and the word co-occurrence is computed to capture the local word consistency. Then, the local information is incorporated into the model for topic discovering. Finally, the generalized EM algorithm is used to estimate the parameters. Extensive experiments on publicly available databases demonstrate the effectiveness of our approach.
关键词Plsa Latent Semantic Model Image Clustering
WOS标题词Science & Technology ; Technology
关键词[WOS]NONLINEAR DIMENSIONALITY REDUCTION ; REPRESENTATION
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000349356300010
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8057
专题模式识别国家重点实验室_图像与视频分析
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.PLA Univ Sci & Technol, Nanjing 210007, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714
Cheng, Jian,Li, Peng,Rui, Ting,et al. Learning latent semantic model with visual consistency for image analysis[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2015,74(4):1341-1356.
APA Cheng, Jian,Li, Peng,Rui, Ting,&Lu, Hanqing.(2015).Learning latent semantic model with visual consistency for image analysis.MULTIMEDIA TOOLS AND APPLICATIONS,74(4),1341-1356.
MLA Cheng, Jian,et al."Learning latent semantic model with visual consistency for image analysis".MULTIMEDIA TOOLS AND APPLICATIONS 74.4(2015):1341-1356.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Learning latent sema(783KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Cheng, Jian]的文章
[Li, Peng]的文章
[Rui, Ting]的文章
百度学术
百度学术中相似的文章
[Cheng, Jian]的文章
[Li, Peng]的文章
[Rui, Ting]的文章
必应学术
必应学术中相似的文章
[Cheng, Jian]的文章
[Li, Peng]的文章
[Rui, Ting]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Learning latent semantic model with visual consistency for image analysis.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

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。