Superpixels and Supervoxels in an Energy Optimization Framework

Veksler, Olga; Boykov, Yuri; Mehrani, Paria

doi:10.1007/978-3-642-15555-0_16

Cited by 319 publications

(273 citation statements)

References 27 publications

Supporting

Mentioning

269

Contrasting

Order By: Relevance

“…According to the number of superpixels in the n-th scale S n , a graph-based segmentation algorithm is used to generate the n-th scale superpixel segmentation result. Graph-based image segmentation algorithms are widely used in superpixel segmentation [59][60][61]. Among these, the entropy rate superpixel (ERS) [61] segmentation method has been demonstrated to be very efficient.…”

Section: Generation Of Multiscale Superpixels In Hsimentioning

confidence: 99%

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Zhang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers.

show abstract

Section: Generation Of Multiscale Superpixels In Hsimentioning

confidence: 99%

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Zhang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Section II outlines the image-adaptive centroidal Voronoi tessellation and compares it with two closely-related algorithms [28], [29]. The statistical segmentation approach is described in Section III.…”

Section: Paper Organizationmentioning

confidence: 99%

“…Of special interest is a class of algorithms which produce a dense oversegmentation of compact clusters, often called superpixels, having relatively uniform size and shape, which furthermore adapt to local intensity edges. Two recent examples are the TurboPixels algorithm of [28] and the Graph Cut superpixels algorithm of [29] which achieve a better balance between the conflicting goals of compactness and boundary adherence than some wellknown image partitioning algorithms which produce segments of highly variable shape and size, like Watershed [32] and Mean-shift [33] algorithms. A large segment of irregular shape is more likely to span more than one object, especially in the absence of boundary cues with insufficient contrast.…”

Section: Image Clustering By Centroidal Voronoi Tessellationmentioning

confidence: 99%

Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

Kéchichian

Valette

Desvignes

et al. 2013

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

To cite this version:Razmig Kéchichian, Sébastien Valette, Michel Desvignes, Rémy Prost. Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2013, 22 (11) Abstract-We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analysis and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared to a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

show abstract

“…But grid analysis might not be an accurate way to represent irregular regions in the image. Therefore, we decided the use of superpixels [4], [21], [26], [27] which group pixels into different regions depending upon their regional size and compactness.…”

Section: Introductionmentioning

confidence: 99%

Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

Haleem

Han

Hemert

et al. 2015

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

• This is the accepted manuscript version of the paper. c 2014 IEEE. Abstract-Scanning Laser Ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide Field of View (FOV), which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.

show abstract

Superpixels and Supervoxels in an Energy Optimization Framework

Cited by 319 publications

References 27 publications

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

Contact Info

Product

Resources

About