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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Object detection by global contour shape
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TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive natural image segmentation via spline regression
IEEE Transactions on Image Processing
Efficient image segmentation using pairwise pixel similarities
Proceedings of the 29th DAGM conference on Pattern recognition
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ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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TurboPixel (TP) is a powerful tool for image over-segmentation. It is fast and can yield a lattice-like structure of superpixel regions with uniform size. This paper presents a method to learn eigen-images from the image to be segmented. Such eigen-images are used to generate the evolution speed in the TP framework. The task is formulated as a problem of pixel clustering. Specifically, for the pixels in each local window, a linear transformation is introduced to map their color vectors to be the cluster indicator vectors. The errors under all such linear transformations are estimated and summed together to obtain an objective function, from which a global optimum is finally obtained. In this process, the eigen-images are constructed. Based upon these eigen-images, multidimensional image gradient operator is defined to evaluate the gradient, which is supplied to the TP algorithm to obtain the final superpixel segmentations. The computational issues are discussed, and an image pyramid is introduced to speed up the computation. Comparative experiments illustrate the effectiveness of our method.