Learning Graph Laplacian for Image Segmentation

Milyaev, Sergey; Barinova, Olga

doi:10.1007/978-3-642-39759-2_7

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…The Laplacian operator has appeared in a multitude of theoretical and applied scenarios leveraging new technologies and improving the effectiveness of existing ones. The possibility of defining the Laplacian operator on graphs has attracted great amount of research in important scientific fields such as graph theory [Diaz et al, 2002, Mohar, 1997, computer vision [Casaca et al, 2011b, Milyaev andBarinova, 2013], computer graphics [Petronetto et al, 2013 and data exploration and visualization [Gomez-Nieto et al, 2013, 2014. In particular, the Laplacian operator plays an important role in clustering graphs, specially due to its good mathematical properties and its capability in promoting visual analysis.…”

Section: Fundamental Conceptsmentioning

confidence: 99%

Graph Laplacian for spectral clustering and seeded image segmentation

Casaca¹,

Taubin²,

Nonato³

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I mage segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy functionals. The effectiveness of both segmentation algorithms is verified by visually evaluating the resulting partitions against state-of-the-art methods as well as through a variety of quantitative measures typically employed as benchmark by the image segmentation community. Our spectral-based segmentation algorithm combines image decomposition, similarity metrics, and spectral graph theory into a concise and powerful framework. An image decomposition is performed to split the input image into texture and cartoon components. Then, an affinity graph is generated and weights are assigned to the edges of the graph according to a gradient-based inner-product function. From the eigenstructure of the affinity graph, the image is partitioned through the spectral cut of the underlying graph. Moreover, the image partitioning can be improved by changing the graph weights by sketching interactively. Visual and numerical evaluation were conducted against representative spectral-based segmentation techniques using boundary and partition quality measures in the well-known BSDS dataset. vii Unlike most existing seed-based methods that rely on complex mathematical formulations that typically do not guarantee unique solution for the segmentation problem while still being prone to be trapped in local minima, our segmentation approach is mathematically simple to formulate, easy-to-implement, and it guarantees to produce a unique solution. Moreover, the formulation holds an anisotropic behavior, that is, pixels sharing similar attributes are preserved closer to each other while big discontinuities are naturally imposed on the boundary between image regions, thus ensuring better fitting on object boundaries. We show that the proposed approach significantly outperforms competing techniques both quantitatively as well as qualitatively, using the classical "GrabCut" dataset from Microsoft as a benchmark. While most of this research concentrates on the particular problem of segmenting an image, we also develop two new techniques to address the problem of image inpainting and photo colorization. Both methods couple the developed segmentation tools with other computer vision approaches in order to operate properly.

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Section: Fundamental Conceptsmentioning

confidence: 99%

Graph Laplacian for spectral clustering and seeded image segmentation

Casaca¹,

Taubin²,

Nonato³

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Comparison of segmentation techniques for histopathological images

Haj-Hassan

Chaddad

Tanougast

et al. 2015

2015 Fifth International Conference on Digital Information and Communication Technology and Its Applications (DICTAP)

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Image segmentation is a widely used in medical imaging applications by detecting anatomical structures and regions of interest. This paper concerns a survey of numerous segmentation model used in biomedical field. We organized segmentation techniques by four approaches, namely, thresholding, edge-based, region-based and snake. These techniques have been compared with simulation results and demonstrated the feasibility of medical image segmentation. Snake was demonstrated a capability with a high performance metrics to detect irregular shape as carcinoma cell type. This study showed the advantage of the deformable segmentation technique to segment abnormal cells with Dice similarity value over 83%.

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Learning Graph Laplacian for Image Segmentation

Cited by 2 publications

References 20 publications

Graph Laplacian for spectral clustering and seeded image segmentation

Graph Laplacian for spectral clustering and seeded image segmentation

Comparison of segmentation techniques for histopathological images

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