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自然场景中多类目标识别的算法研究
Alternative TitleMulti-Class Object Recognition in Natural Scenes
吴士林; 朱枫
Department光电信息技术研究室
Source Publication计算机工程与科学
ISSN1007-130X
2012
Volume34Issue:3Pages:91-95
Indexed ByCSCD
CSCD IDCSCD:4503568
Contribution Rank1
Keyword目标识别 多类 图像分割 Joint-boost算法
Abstract为了实现复杂自然场景中多类目标的识别与分割,本文利用条件概率模型(CM)对目标特征进行建模,融合了纹理特征、纹理环境特征和位置特征,并采用场景类别对各类目标间的相互约束关系进行建模,在此基础上研究基于场景类别的条件概率模型(sCM)在多类目标识别与分割中的应用。本文选用Oliva&Torralba数据库对模型进行实验并与国外其他方法进行了比较。实验结果表明,该算法在多类目标识别与分割中取得很好的结果,在提高总体识别率的同时提高了物体边缘部分识别与分割的正确率,更有效地提高了视觉效果。
Other AbstractIn this paper, a conditional model (CM) is used to incorporate different feature potentials including texture, texture environment and location features of objects for multi-class object recognition and segmentation in complex natural images. Besides, we model the relationship between different objects by the scene of images and propose a new scene-based conditional model called the sCM model. We investigate the performance of our model in the class-based pixel-wise segmentation of images on the Oliva & Torralba database and compare its result with other methods. The results show that our theme-based R-CRF model significantly improves the accuracy of objects in the whole database. More significantly, a large perceptual improvement is gained, i.e. the details of different objects are correctly labeled. 
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/10063
Collection光电信息技术研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院研究生院
3.中国科学院光电信息处理重点实验室
4.辽宁省图像理解与视觉计算重点实验
Recommended Citation
GB/T 7714
吴士林,朱枫. 自然场景中多类目标识别的算法研究[J]. 计算机工程与科学,2012,34(3):91-95.
APA 吴士林,&朱枫.(2012).自然场景中多类目标识别的算法研究.计算机工程与科学,34(3),91-95.
MLA 吴士林,et al."自然场景中多类目标识别的算法研究".计算机工程与科学 34.3(2012):91-95.
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