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
Superpixels: An evaluation of the state-of-the-art
TL;DR: An overall ranking of superpixel algorithms is presented which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of the authors' benchmark at http://www.davidstutz.de/projects/superpixel-benchmark/ .
Proceedings ArticleDOI
NeuFlow: A runtime reconfigurable dataflow processor for vision
TL;DR: A scalable dataflow hardware architecture optimized for the computation of general-purpose vision algorithms — neuFlow — and a dataflow compiler — luaFlow — that transforms high-level flow-graph representations of these algorithms into machine code for neu Flow are presented.
Posted Content
Indoor Semantic Segmentation using depth information
TL;DR: This work addresses multi-class segmentation of indoor scenes with RGB-D inputs by applying a multiscale convolutional network to learn features directly from the images and the depth information.
Posted Content
From Image-level to Pixel-level Labeling with Convolutional Networks
TL;DR: In this paper, the authors propose a weakly supervised object segmentation model, which is constrained during training to put more weight on pixels which are important for classifying the image.
Journal ArticleDOI
Convolutional networks can learn to generate affinity graphs for image segmentation
Srinivas C. Turaga,Joseph F. Murray,Viren Jain,Fabian Roth,Moritz Helmstaedter,Kevin L. Briggman,Winfried Denk,H. Sebastian Seung +7 more
TL;DR: This work presents a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts and shows that the CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions.
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.