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题名: 自然场景中多类目标识别的算法研究
其他题名: Multi-Class Object Recognition in Natural Scenes
作者: 吴士林 ; 朱枫
作者部门: 光电信息技术研究室
关键词: 目标识别 ; 多类 ; 图像分割 ; Joint-boost算法
刊名: 计算机工程与科学
ISSN号: 1007-130X
出版日期: 2012
卷号: 34, 期号:3, 页码:91-95
收录类别: CSCD
产权排序: 1
摘要: 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。
英文摘要: In 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. 
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/10063
Appears in Collections:光电信息技术研究室_期刊论文

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