Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
A survey of graph layout problems
ACM Computing Surveys (CSUR)
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilabel Random Walker Image Segmentation Using Prior Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Isoperimetric Graph Partitioning for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Graph Partitioning by Spectral Rounding: Applications in Image Segmentation and Clustering
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Color Texture Modeling and Color Image Decomposition in a Variational-PDE Approach
SYNASC '06 Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Automatic Image Segmentation by Tree Pruning
Journal of Mathematical Imaging and Vision
Spectral Graph Theory and its Applications
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
High Accuracy Bicubic Interpolation Using Image Local Features
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A Normalized Cuts Based Image Segmentation Method
ICIC '09 Proceedings of the 2009 Second International Conference on Information and Computing Science - Volume 02
Benchmarking Image Segmentation Algorithms
International Journal of Computer Vision
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Image Segmentation Using Component Tree and Normalized Cut
SIBGRAPI '10 Proceedings of the 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images
Image segmentation using quadtree-based similarity graph and normalized cut
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Segmentation Using Cartoon-Texture Decomposition and Inner Product-Based Metric
SIBGRAPI '11 Proceedings of the 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Image segmentation is an indispensable tool in computer vision applications, such as recognition, detection and tracking. In this work, we introduce a novel user-assisted image segmentation technique which combines image decomposition, inner product-based similarity metric, and spectral graph theory into a concise and unified framework. First, we perform an image decomposition to split the image into texture and cartoon components. Then, an affinity graph is generated and the weights are assigned to its edges according to a gradient-based inner-product function. From the eigenstructure of the affinity graph, the image is partitioned through the spectral cut of the underlying graph. The computational effort of our framework is alleviated by an image coarsening process, which reduces the graph size considerably. Moreover, the image partitioning can be improved by interactively changing the graph weights by sketching. Finally, a coarse-to-fine interpolation is applied in order to assemble the partition back onto the original image. The efficiency of the proposed methodology is attested by comparisons with state-of-art spectral segmentation methods through a qualitative and quantitative analysis of the results.