Shape-Based Image Segmentation Using Normalized Cuts

Cai, Wenchao; Wu, Jue; Chung, Albert S.

doi:10.1109/icip.2006.312748

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…The multilevel representation can be accomplished using conventional multiscale approaches based on a combination of graph compression and cross-scale constraint [11], shape information with PCA [13], and texture descriptors [26].…”

Section: Related Work and Basic Conceptsmentioning

confidence: 99%

“…For example, the accuracy in detecting the boundaries between image regions is highly dependent on the weights assigned to the edges of the graph. Although automatic schemes have been proposed to compute those weights [1], [10], [11], [12], [13], it is well known that user intervention is essential in many cases to accurately define the object boundaries [14]. Therefore, incorporating user knowledge into the segmentation process is of paramount importance.…”

Section: Introductionmentioning

confidence: 99%

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Spectral Segmentation Using Cartoon-Texture Decomposition and Inner Product-Based Metric

Casaca

Paiva

Nonato

2011

2011 24th SIBGRAPI Conference on Graphics, Patterns and Images

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This paper presents a user-assisted image partition technique that combines cartoon-texture decomposition, inner product-based similarity metric, and spectral cut into a unified framework. The cartoon-texture decomposition is used to first split the image into textured and texture-free components, the latter being used to define a gradient-based inner-product function. An affinity graph is then derived and weights are assigned to its edges according to the inner product-based metric. Spectral cut is computed on the affinity graph so as to partition the image. The computational burden of the spectral cut is mitigated by a fineto-coarse image representation process, which enables moderate size graphs that can be handled more efficiently. The partitioning can be steered by interactively by changing the weights of the graph through user strokes. Weights are updated by combining the texture component computed in the first stage of our pipeline and a recent harmonic analysis technique that captures waving patterns. Finally, a coarse-to-fine interpolation is applied in order to project the partition back onto the original image. The suitable performance of the proposed methodology is attested by comparisons against state-of-art spectral segmentation methods.

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Section: Related Work and Basic Conceptsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral Segmentation Using Cartoon-Texture Decomposition and Inner Product-Based Metric

Casaca

Paiva

Nonato

2011

2011 24th SIBGRAPI Conference on Graphics, Patterns and Images

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“…Graph-based image segmentation has witnessed an explosion of interest in recent years [1][2][3][4][5][6][7][8][9]. However, this image segmentation technique is typically suffers memory overhead and time consuming drawbacks [10].…”

Section: Introductionmentioning

confidence: 99%

A color histogram measure and its application to image segmentation

Zheng

Shi

et al. 2013

2013 International Conference on Machine Learning and Cybernetics

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“…In [39], a motion layer segmentation method was introduced to remove relatively large objects from the original video. Additional video completion papers based on figure-background segmentation include ( [7], [27], [28], [4], [29]). However, at the present time, none of these methods exploits the underlying global spatio-temporal dynamics of the sequence.…”

Section: Introductionmentioning

confidence: 99%

A Rank Minimization Approach to Video Inpainting

Ding

Sznaier

Camps

2007

2007 IEEE 11th International Conference on Computer Vision

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This paper addresses the problem of video inpainting, that is seamlessly reconstructing missing portions in a set of video frames. We propose to solve this problem proceeding as follows: (i) finding a set of descriptors that encapsulate the information necessary to reconstruct a frame, (ii) finding an optimal estimate of the value of these descriptors for the missing/corrupted frames, and (iii) using the estimated values to reconstruct the frames. The main result of the paper shows that the optimal descriptor estimates can be efficiently obtained by minimizing the rank of a matrix directly constructed from the available data, leading to a simple, computationally attractive, dynamic inpainting algorithm that optimizes the use of spatio/temporal information. Moreover, contrary to most currently available techniques, the method can handle non-periodic target motions, non-stationary backgrounds and moving cameras. These results are illustrated with several examples, including reconstructing dynamic textures and object disocclusion in cases involving both moving targets and camera.

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Shape-Based Image Segmentation Using Normalized Cuts

Cited by 13 publications

References 13 publications

Spectral Segmentation Using Cartoon-Texture Decomposition and Inner Product-Based Metric

Spectral Segmentation Using Cartoon-Texture Decomposition and Inner Product-Based Metric

A color histogram measure and its application to image segmentation

A Rank Minimization Approach to Video Inpainting

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