State-of-the-art and future challenges in video scene detection: a survey

Fabro, Manfred Del; Böszörményi, László

doi:10.1007/s00530-013-0306-4

Cited by 83 publications

(37 citation statements)

References 65 publications

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“…State-of-the-art video segment detection methods use multiple modalities only infrequently [4]. When cross-modal alignment is performed, it is generally at a lower-level of granularity than needed here.…”

Section: Multimodal Segmentationmentioning

confidence: 97%

Multi-factor segmentation for topic visualization and recommendation

Bhatt

Popescu-Belis

Habibi

et al. 2013

Proceedings of the 21st ACM International Conference on Multimedia

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This paper presents the MUST-VIS system for the MediaMixer/VideoLectures.NET Temporal Segmentation and Annotation Grand Challenge. The system allows users to visualize a lecture as a series of segments represented by keyword clouds, with relations to other similar lectures and segments. Segmentation is performed using a multi-factor algorithm which takes advantage of the audio (through automatic speech recognition and word-based segmentation) and video (through the detection of actions such as writing on the blackboard). The similarity across segments and lectures is computed using a content-based recommendation algorithm. Overall, the graph-based representation of segment similarity appears to be a promising and cost-effective approach to navigating lecture databases.

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Section: Multimodal Segmentationmentioning

confidence: 97%

Multi-factor segmentation for topic visualization and recommendation

Bhatt

Popescu-Belis

Habibi

et al. 2013

Proceedings of the 21st ACM International Conference on Multimedia

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“…Another technique for temporal segmentation is scene segmentation. An overview about this state-of-the-art techniques can be found in [9]. The authors divide the approaches in three main categories and combinations of these.…”

Section: Related Workmentioning

confidence: 98%

Segmentation and Indexing of Endoscopic Videos

Primus

2014

Proceedings of the 22nd ACM International Conference on Multimedia

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Over the last few years it has become common to archive video recordings of endoscopic surgeries. These videos are of high value for medics, junior doctors, patients and hospital management but currently they are used rarely or not at all. Each day tens to hundreds of hours of new videos are added to archives without metadata that would support content-based search. In order to fully utilize these videos it is necessary to analyze the content of the recordings. Endoscopic videos are in some aspects fundamentally different to other types of videos. Therefore, pre-existing content-based analysis methods must be tested for their ability to operate with this kind of video and, if required, they must be adopted or new methods must be found. Especially, we address video segmentation and indexing in this work. We present our preliminary work and ideas for future work to add contentbased information to endoscopic videos.

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“…Embora amplamente utilizadas em trabalhos seminais na área [Del Fabro and Boszormenyi, 2013], as métricas de Precisão e Abrangência possuem limitações quanto a segmentação em cenas. Nesse sentido, a Figura 5.11 ilustra duas técnicas que obtém, para um groundtruth com duas transições (indicado como Confiável na Figura 5.11), diferentes segmentações.…”

Section: Avaliaçãounclassified

Recuperação de Informação Multimídia em Big Data Utilizando OpenCV Python

Goularte¹,

Trojahn²,

Kishi³

2019

Minicursos Do XXV Simpósio Brasileiro De Sistemas Multimídia E Web

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The popularization of systems, applications and devices to produce, view and share multimedia, saw the need to treat a large volume of data arise. In related areas (such as Multimedia Big Data, Data Science and Multimedia Information Retrieval) a key step is commonly referred as Multimedia Indexing or Multimedia Big Data Analysis, where the aim is to represent multimedia content into smaller, more manageable units, allowing the extraction of data features and information essential to the proper performance of the associated services. This mini-course discusses current tools and techniques for indexing, extracting and processing of multimodal multimedia content. The techniques are exemplified in Python OpenCV over different content (like images, audio, text and video), leading to the interest of services like Netflix, Google and YouTube on this subject, attracting the interest of researchers and developers. ResumoA popularização de sistemas, aplicativos e dispositivos para produzir, exibir e compartilhar conteúdo multimídia fez surgir a necessidade de tratar um grande volume de dados. Nas áreas relacionadas (como Multimedia Big Data, Ciência de Dados e Recuperação de Informação Multimídia) um pré-requisito chave é comumente conhecido como Indexação Multimídia (ou Análise Multimídia em Big Data), onde o objetivo é representar o conteúdo em unidades menores e mais gerenciáveis, permitindo a extração de features dos dados e informações essenciais para o bom funcionamento dos serviços associados. Este minicurso aborda ferramentas e técnicas atuais para indexação, extração e processamento de conteúdo multimídia multimodal. As técnicas XXV Simpósio Brasileiro de Sistemas Multimídia e Web: Minicursos Listagem 5.1. Exemplo de código Python utilizando OpenCV. Arquivo exemplo1.py XXV Simpósio Brasileiro de Sistemas Multimídia e Web: Minicursos

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State-of-the-art and future challenges in video scene detection: a survey

Cited by 83 publications

References 65 publications

Multi-factor segmentation for topic visualization and recommendation

Multi-factor segmentation for topic visualization and recommendation

Segmentation and Indexing of Endoscopic Videos

Recuperação de Informação Multimídia em Big Data Utilizando OpenCV Python

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