Object-based RGBD Image Co-segmentation with Mutex Constraint

被引:0
作者
Fu, Huazhu [1 ]
Xu, Dong [1 ]
Lin, Stephen [2 ]
Liu, Jiang [3 ]
机构
[1] Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore
[2] Microsoft Res, Beijing, Peoples R China
[3] Agcy Sci Technol & Res, Inst Infocomm Res, Singapore, Singapore
来源
2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2015年
关键词
SALIENCY DETECTION; OPTIMIZATION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present an object-based co-segmentation method that takes advantage of depth data and is able to correctly handle noisy images in which the common foreground object is missing. With RGBD images, our method utilizes the depth channel to enhance identification of similar foreground objects via a proposed RGBD co-saliency map, as well as to improve detection of object-like regions and provide depth based local features for region comparison. To accurately deal with noisy images where the common object appears more than or less than once, we formulate co-segmentation in a fully-connected graph structure together with mutual exclusion (mutex) constraints that prevent improper solutions. Experiments show that this object-based RGBD co-segmentation with mutex constraints outperforms related techniques on an RGBD co-segmentation dataset, while effectively processing noisy images. Moreover, we show that this method also provides performance comparable to state-of-the-art RGB co-segmentation techniques on regular RGB images with depth maps estimated from them.
引用
收藏
页码:4428 / 4436
页数:9
相关论文
共 38 条
  • [1] [Anonymous], NIPS
  • [2] Multiscale Combinatorial Grouping
    Arbelaez, Pablo
    Pont-Tuset, Jordi
    Barron, Jonathan T.
    Marques, Ferran
    Malik, Jitendra
    [J]. 2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, : 328 - 335
  • [3] Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance
    Batra, Dhruv
    Kowdle, Adarsh
    Parikh, Devi
    Luo, Jiebo
    Chen, Tsuhan
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2011, 93 (03) : 273 - 292
  • [4] Bo LF, 2011, IEEE INT C INT ROBOT, P821, DOI 10.1109/IROS.2011.6048717
  • [5] Co-saliency Detection via Base Reconstruction
    Cao, Xiaochun
    Cheng, Yupeng
    Tao, Zhiqiang
    Fu, Huazhu
    [J]. PROCEEDINGS OF THE 2014 ACM CONFERENCE ON MULTIMEDIA (MM'14), 2014, : 997 - 1000
  • [6] Self-Adaptively Weighted Co-Saliency Detection via Rank Constraint
    Cao, Xiaochun
    Tao, Zhiqiang
    Zhang, Bao
    Fu, Huazhu
    Feng, Wei
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2014, 23 (09) : 4175 - 4186
  • [7] CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
    Carreira, Joao
    Sminchisescu, Cristian
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2012, 34 (07) : 1312 - 1328
  • [8] Cheng Y., 2014, ICIMCS, P23
  • [9] Histograms of oriented gradients for human detection
    Dalal, N
    Triggs, B
    [J]. 2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS, 2005, : 886 - 893
  • [10] Category-Independent Object Proposals with Diverse Ranking
    Endres, Ian
    Hoiem, Derek
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2014, 36 (02) : 222 - 234