Weighted Adaptive Neighborhood Hypergraph Partitioning for Image Segmentation

Rital, Soufiane; Cherifi, Hocine; Miguet, Serge

doi:10.1007/11552499_58

Cited by 29 publications

(14 citation statements)

References 16 publications

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“…Future Directions: With the development of tensor algebra and hypergraph spectra, more opportunities are emerging to explore HGSP and its applications. One interesting topic is how to construct the hypergraph efficiently, where distancebased and model-based methods have achieved significant successes in specific areas, such as image processing [93] and natural language processing [94]. Another promising direction is to apply HGSP in analyzing and optimizing the multilayer networks.…”

Section: Discussionmentioning

confidence: 99%

Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications

Zhang

Ding

Cui

2020

IEEE Internet Things J.

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Signal processing over graphs has recently attracted significant attention for dealing with the structured data. Normal graphs, however, only model pairwise relationships between nodes and are not effective in representing and capturing some high-order relationships of data samples, which are common in many applications, such as Internet of Things (IoT). In this article, we propose a new framework of hypergraph signal processing (HGSP) based on the tensor representation to generalize the traditional graph signal processing (GSP) to tackle highorder interactions. We introduce the core concepts of HGSP and define the hypergraph Fourier space. We then study the spectrum properties of hypergraph Fourier transform (HGFT) and explain its connection to mainstream digital signal processing. We derive the novel hypergraph sampling theory and present the fundamentals of hypergraph filter design based on the tensor framework. We present HGSP-based methods for several signal processing and data analysis applications. Our experimental results demonstrate significant performance improvement using our HGSP framework over some traditional signal processing solutions.

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Section: Discussionmentioning

confidence: 99%

Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications

Zhang

Ding

Cui

2020

IEEE Internet Things J.

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“…In [28], the authors proposed a method based on neighborhood hypergraph partitioning. The experiments have demonstrated the proper functioning of the method and its performance compared to the normalized cut Ncut) algorithm.…”

Section: Related-work To the Segmentation Of Brain Regionsmentioning

confidence: 99%

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

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Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

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“…Network modelling puts into relation different entities, therefore it has naturally become a powerful tool to capture the elements articulation in stories (Rital et al 2005;Park et al 2012;Waumans et al 2015;Tan et al 2014;Mish 2016;Mourchid et al 2018;Viard and Fournier-S'niehotta 2018;Markovič et al 2018). Such network models have been applied to many different types of stories, starting with written stories in books (Waumans et al 2015;Markovič et al 2018), in news events from news papers and TV , in television series (Tan et al 2014), and eventually in the target medium of this paper: movies (Park et al 2012;Mourchid et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Such network models have been applied to many different types of stories, starting with written stories in books (Waumans et al 2015;Markovič et al 2018), in news events from news papers and TV , in television series (Tan et al 2014), and eventually in the target medium of this paper: movies (Park et al 2012;Mourchid et al 2018). The topology and structure of these networks have been investigated both visually ) and analytically (Waumans et al 2015;Rital et al 2005), and may in turn be used for prediction tasks (Viard and Fournier-S'niehotta 2018). These narrative networks built from large scale archives can be automatically created (Waumans et al 2015; or use manual annotations (Mish 2016).…”

Section: Introductionmentioning

confidence: 99%

“…After discussing the related work in the next section, we introduce the proposed model called Movienet in "Modeling stories with Movienet" section. We describe how we extract the multilayer network in "Extracting the multilayer network" section, before deploying the analysis in "Network analysis" section on the Star Wars saga (Lucas 1977;1980;1983;1999;2002;2005). We finally conclude in "Conclusion" section.…”

Section: Introductionmentioning

confidence: 99%

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Movienet: a movie multilayer network model using visual and textual semantic cues

et al. 2019

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Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc.. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.

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Weighted Adaptive Neighborhood Hypergraph Partitioning for Image Segmentation

Cited by 29 publications

References 16 publications

Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications

Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Movienet: a movie multilayer network model using visual and textual semantic cues

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