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
Proceedings ArticleDOI
Segmentation from a box
TL;DR: A study of 14 subjects who are asked to segment a boxed target in a set of 50 real images for which they have no semantic attachment finds that the subjects perceive and trace almost the same segmentations as each other, despite the inhomogeneity of the image intensities, irregular shapes of the segmentation targets and weakness of the target boundaries.
Proceedings ArticleDOI
Automatic building detection in aerial and satellite images
Parvaneh Saeedi,Harold Zwick +1 more
TL;DR: The development of an automated roof detection system from single monocular electro-optic satellite imagery capable of detecting small gabled residential rooftops with variant light reflection properties with high positional accuracies is described.
Journal ArticleDOI
Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation
TL;DR: An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed and Experimental results on challenging benchmark video sequences reveal the superior performance of the algorithm when confronting appearance changes and background clutters.
Journal ArticleDOI
Template-Cut: A Pattern-Based Segmentation Paradigm
TL;DR: In this paper, a template-based segmentation approach is proposed to separate an object from the background in which the nodes are sampled non-uniformly and non-equidistantly on the image.
Proceedings ArticleDOI
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping *
TL;DR: In this article, an unsupervised method is proposed to detect and segment portions of images of live scenes that, at some point in time, are seen moving as a coherent whole, referred to as objects.
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.