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FatRegion: A Fast Adaptive Tree-Structured Region Extraction Approach

文献类型：期刊论文

作者Xing, Junliang1; Hu, Weiming2,3; Ai, Haizhou4; Yan, Shuicheng5
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2018-03-01
卷号28期号:3页码:601-615
关键词Object Classification Object Segmentation Object Tracking Region Extraction Superpixel
DOI10.1109/TCSVT.2016.2615466
文献子类Article
英文摘要Coherent image regions can be used as good features for many computer vision tasks, such as object tracking, segmentation, and recognition. Most of previous region extraction methods, however, are not suitable for online applications because of their either heavy computations or unsatisfactory results. We propose a seed-based region growing and merging approach to generate simultaneously coherent and discriminative image regions. We present a quadtree-based seed initialization algorithm to adaptively place seeds into different image areas and then grow them into regions by a color-and edge-guided growing procedure. To merge these regions in different levels, we propose to use the generalized boundary strength to measure the quality of region merging result. In addition, we present a region merging algorithm of linear time complexity to perform efficient and effective region merging. Overall, our new approach simultaneously holds these advantages: 1) it is extremely fast with linear complexity in both time and space, which takes less than 50 ms to process an HVGA image; 2) it can give a direct control of the region number and well adapt to image regions with various sizes and shapes; and 3) it provides a tree-structured representation of the regions and thus can model the image from multiple scales. We evaluate the proposed approach on the standard benchmarks with extensive comparisons with the state-of-the-art methods. The experimental results demonstrate its good comprehensive performances. Example applications using the extracted regions as features for online object tracking and multiclass object segmentation also exhibit its potential for many computer vision tasks.
WOS关键词IMAGE SEGMENTATION ; GRAPH CUTS ; TEXTURE SEGMENTATION ; ENERGY MINIMIZATION ; CLASSIFICATION ; MODEL
WOS研究方向Engineering
语种英语
WOS记录号WOS:000426693100004
资助机构973 Basic Research Program of China(2014CB349303) ; Natural Science Foundation of China(61303178 ; CAS(XDB02070003) ; CAS ; U1636218 ; 61472421)
源URL[http://ir.ia.ac.cn/handle/173211/21978]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Tech, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
4.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China
5.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
推荐引用方式
GB/T 7714		 Xing, Junliang,Hu, Weiming,Ai, Haizhou,et al. FatRegion: A Fast Adaptive Tree-Structured Region Extraction Approach[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2018,28(3):601-615.
APA Xing, Junliang,Hu, Weiming,Ai, Haizhou,&Yan, Shuicheng.(2018).FatRegion: A Fast Adaptive Tree-Structured Region Extraction Approach.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,28(3),601-615.
MLA Xing, Junliang,et al."FatRegion: A Fast Adaptive Tree-Structured Region Extraction Approach".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28.3(2018):601-615.

入库方式： OAI收割

来源：自动化研究所

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