Movie scenes detection with MIGSOM based on shots semi-supervised clustering

Ayadi, Thouraya; Ellouze, Mehdi; Hamdani, Tarek M.; Alimi, Adel M.

doi:10.1007/s00521-012-0930-5

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…Moreover, we can use the neighborhood property in selforganizing maps to have a better understanding of the relationships between agglomerations of nodes. We have used this tool in our previous works, in different contexts and the results were very encouraging [45,46].…”

Section: Detection Of Agglomerationsmentioning

confidence: 90%

Social Network Community Detection by Combining Self‐Organizing Maps and Genetic Algorithms

Ellouze

2021

Complexity

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Social networks have become an important source of information from which we can extract valuable indicators that can be used in many fields such as marketing, statistics, and advertising among others. To this end, many research works in the literature offer users some tools that can help them take advantage of this mine of information. Community detection is one of these tools and aims to detect a set of entities that share some features within a social network. We have taken part in this effort, and we proposed an approach mainly based on pattern recognition techniques. The novelty of this approach is that we do not directly tackle the social networks to find these communities. We rather proceeded in two stages; first, we detected community cores through a special type of self-organizing map called the Growing Hierarchical Self-Organizing Map (GHSOM). In the second stage, the agglomerations resulting from GHSOM were grouped to retrieve the final communities. The quality of the final partition would be under the control of an evaluation function that is maximized through genetic algorithms. Our system was tested on real and artificial databases, and the obtained results are really encouraging.

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Section: Detection Of Agglomerationsmentioning

confidence: 90%

Social Network Community Detection by Combining Self‐Organizing Maps and Genetic Algorithms

Ellouze

2021

Complexity

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“…A Self-Organizing Map [20,21] is an abstract mathematical model of topographic mapping from the visual sensors to the cerebral cortex. When presented with a stimulus, neurons compete among themselves for possession or ownership of this input.…”

Section: Unsupervised Frame Clusteringmentioning

confidence: 99%

“…The threshold in the window is calculated by following equation: (21) Where Td Threshold With Itti's Method A number of parameters are required to perform the filtering process which including wavelength of sinusoid ( ), the orientation of the filter ( ), the phase offset ( ), and the spatial aspect ratio.…”

Section: Chapter 4 : Proposed Video Summarization Algorithmmentioning

confidence: 99%

The impact of colour visual attention for video summarization

Qian¹

2023

Preprint

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A High Definition visual attention based video summarization algorithm is proposed to extract feature frames and create a video summary. Specifically, the proposed framework is used as the basis for establishing whether or not there is a measurable impact on summaries constructed when choosing to incorporate visual attention mechanisms into the processing pipeline. The algorithm was assessed against manual human generated key-frame summaries presented with tested datasets from the Open Video Dataset (www.open-video.org). Of the frames selected by the algorithm, up to 68.1% were in agreement with the manual frame summaries depending on the category and length of the video. Specifically, a clear impact of agreement rate with the ground truth is demonstrated when including colour-attention models (in general) into the summarization framework, with the proposed colour-attention model achieving stronger agreement with human selected summaries, than other models from the literature.

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“…Generally speaking, automatic scene boundary detection techniques can be categorized into following classes, i.e. graph based [Ayadi et al 2012;del Fabro and Boszormenyi 2010;Mezaris, Sidiropoulos, Dimou and Kompatsiaris 2010;Sakarya and Telatar 2010;Sakarya et al 2012;Seeling 2010;Sidiropoulos, Mezaris, Kompatsiaris, Meinedo, Bugalho and Trancoso 2011;Su et al 2012;, film editing technique based [Choroś and Pawlaczyk 2010;Zhu and Liang 2011], statistics learning based [Baber, Afzulpurkar and Bakhtyar 2011;Chao, Changsheng, Jian and Hanqing 2011;Ellouze, Boujemaa and Alimi 2010;Huang and Zhang 2010;Mohanta et al 2010;Sang and Xu 2010;Seung-Bo et al 2010;Tjondronegoro and Chen 2010;Wilson et al 2010;Zeng et al 2010], and multi-features based [Dumont and Quénot 2012;Ercolessi et al 2011;Heejun and Jaesoo 2011;Huang and Tung 2010;Hui and Cuihua 2010;Mitrović, Hartlieb, Zeppelzauer and Zaharieva 2010;Poulisse et al 2012].…”

Section: Semantic Temporal Segmentationmentioning

confidence: 99%

MAC-REALM: A Video Content Feature Extraction and Modelling Framework

Parmar

Angelides

2015

The Computer Journal

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This paper discusses MAC-REALM, a framework for extraction of syntactic and semantic content features and content modelling with either little or no user interaction. The framework integrates a four filter-plane strategy: a pre-processing plane that filters redundant data, a syntactic feature extraction plane that filters syntactic features, a semantic relationships analysis and linkage plane that filters the spatial and temporal relationships of content features, and finally a content modelling plane where the syntactic and semantic content features are integrated into a content model. Each of the four planes is split into three layers: the content layer, where the content to be processed is stored, the application layer, where the content is converted into content descriptions, and the MPEG-7 layer, where content descriptions are serialized. Using MPEG-7 standards to produce the content model will provide wideranging interoperability, while facilitating granular multi-content type searches. MAC-REALM aims at 'bridging' the semantic gap, by integrating the syntactic and semantic content features from extraction through to modelling.

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Movie scenes detection with MIGSOM based on shots semi-supervised clustering

Cited by 9 publications

References 33 publications

Social Network Community Detection by Combining Self‐Organizing Maps and Genetic Algorithms

Social Network Community Detection by Combining Self‐Organizing Maps and Genetic Algorithms

The impact of colour visual attention for video summarization

MAC-REALM: A Video Content Feature Extraction and Modelling Framework

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