Efficient Graph-Based Image Segmentation
TLDR
An efficient segmentation algorithm is developed based on a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image and it is shown that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties.Abstract:
This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.read more
Citations
More filters
Journal ArticleDOI
Mixture-Based Superpixel Segmentation and Classification of SAR Images
Sertac Arisoy,Koray Kayabol +1 more
TL;DR: It is shown that the results obtained by the proposed superpixel method are capable of achieving a more accurate classification compared with those obtained by state-of-the-art superpixel segmentation methods such as quick-shift, turbo pixels, simple linear iterative clustering, and pixel intensity and location similarity.
Journal ArticleDOI
Geometrically Induced Force Interaction for Three-Dimensional Deformable Models
TL;DR: A novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions is proposed, and it is shown that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable models can effectively overcome image noise.
Proceedings ArticleDOI
Object proposal by multi-branch hierarchical segmentation
TL;DR: This work proposes a novel multi-branch hierarchical segmentation approach that alleviates problems by learning multiple merging strategies in each step in a complementary manner, such that errors in one merging strategy could be corrected by the others.
Journal ArticleDOI
Efficient Shadow Removal Using Subregion Matching Illumination Transfer
TL;DR: A novel shadow removal algorithm is presented by performing illumination transfer on the matched subregion pairs between the shadow regions and non-shadow regions, and this method can process complex images with different kinds of shadowed texture regions and illumination conditions.
Proceedings ArticleDOI
Complexity-adaptive distance metric for object proposals generation
TL;DR: A novel distance metric for grouping two superpixels in high-complexity scenarios is developed and combined with existing distance metrics, a complexity-adaptive distance measure is produced that achieves improved grouping in different levels of complexity.
References
More filters
Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Proceedings ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
TL;DR: A family of graph-theoretical algorithms based on the minimal spanning tree are capable of detecting several kinds of cluster structure in arbitrary point sets; description of the detected clusters is possible in some cases by extensions of the method.