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
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Journal ArticleDOI
TurboPixels: Fast Superpixels Using Geometric Flows
Alex Levinshtein,A. Stere,Kiriakos N. Kutulakos,David J. Fleet,Sven Dickinson,Kaleem Siddiqi +5 more
TL;DR: A geometric-flow-based algorithm for computing a dense oversegmentation of an image, often referred to as superpixels, which yields less undersegmentation than algorithms that lack a compactness constraint while offering a significant speedup over N-cuts, which does enforce compactness.
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Robust higher order potentials for enforcing label consistency
TL;DR: This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner based on higher order conditional random fields and uses potentials defined on sets of pixels generated using unsupervised segmentation algorithms.
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Salient Object Detection: A Discriminative Regional Feature Integration Approach
TL;DR: This paper regards saliency map computation as a regression problem, which is based on multi-level image segmentation, and uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the salency map.
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ClassCut for unsupervised class segmentation
TL;DR: A novel method for unsupervised class segmentation on a set of images that alternates between segmenting object instances and learning a class model based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation.
Proceedings ArticleDOI
Structured Forests for Fast Edge Detection
Piotr Dollár,C. Lawrence Zitnick +1 more
TL;DR: This paper forms the problem of predicting local edge masks in a structured learning framework applied to random decision forests and develops a novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated.
References
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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.