A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-layered image-based rendering
Proceedings of the 1999 conference on Graphics interface '99
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Composition by Spatiotemporal Object Segmentation, 3D-Structure and Tracking
IV '99 Proceedings of the 1999 International Conference on Information Visualisation
Unsupervised Semantic Object Segmentation of Stereoscopic Video Sequences
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
High-quality video view interpolation using a layered representation
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Circuits and Systems for Video Technology
Multi-view video based multiple objects segmentation using graph cut and spatiotemporal projections
Journal of Visual Communication and Image Representation
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In this paper we propose an unsupervised multiview image segmentation algorithm, combining multiple image cues including color, depth, and motion. First, the interested objects are extracted by computing a saliency map based on the visual attention model. By analyzing the saliency map, we automatically obtain the number of foreground objects and their bounding boxes, which are used to initialize the segmentation algorithm. Then the optimal segmentation is calculated by energy minimization under the min-cut/max-flow theory. There are two major contributions in this paper. First, we show that the performance of graph cut segmentation depends on the user interactive initialization, while our proposed method provides robust initialization instead of the random user input. In addition, we propose a novel energy function with a locally adaptive smoothness term when constructing the graphs. Experimental results demonstrate that subjectively good segmentation results are obtained.