The IMCOP System for Data Enrichment and Content Discovery and Delivery

Baran, Remigiusz; Zeja, Andrzej

doi:10.1109/csci.2015.137

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…The framework hosts several high-and low-level meta-data extraction algorithms (referring to our previous research conducted within the scope of, e.g. IMCOP project [1]) that include detection of the anchor-person, identification of daytime and nighttime shootings and extraction of lowlevel video quality indicators.…”

Section: Different Components Of Amis 21 Video Summarizationmentioning

confidence: 99%

Summarizing videos into a target language: Methodology, architectures and evaluation

Smaïli¹,

Fohr²,

González-Gallardo

et al. 2019

IFS

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The aim of the work is to report the results of the Chist-Era project AMIS (Access Multilingual Information opinionS). The purpose of AMIS is to answer the following question: How to make the information in a foreign language accessible for everyone? This issue is not limited to translate a source video into a target language video since the objective is to provide only the main idea of an Arabic video in English. This objective necessitates developing research in several areas that are not, all arrived at a maturity state: Video summarization, Speech recognition, Machine translation, Audio summarization and Speech segmentation. In this article we present several possible architectures to achieve our objective, yet we focus on only one of them. The scientific locks are be presented, and we explain how to deal with them. One of the big challenges of this work is to conceive a way to evaluate objectively a system composed of several components knowing that each of them has its limits and can propagate errors through the first component. Also, a subjective evaluation procedure is proposed in which several annotators have been mobilized to test the quality of the achieved summaries.

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Section: Different Components Of Amis 21 Video Summarizationmentioning

confidence: 99%

Summarizing videos into a target language: Methodology, architectures and evaluation

Smaïli¹,

Fohr²,

González-Gallardo

et al. 2019

IFS

View full text Add to dashboard Cite

show abstract

“…The framework hosts several high-and low-level meta-data extraction algorithms (referring to our former research conducted within the scope of e.g. IMCOP project [1]) that include detection of the anchor-person, recognition of day and night shots and extraction of low-level video quality indicators. The main summarization processes start with Shot Boundary Detection (SBD).…”

Section: Video Summarizationmentioning

confidence: 99%

A First Summarization System of a Video in a Target Language

Smaïli

Fohr

González-Gallardo

et al. 2018

Cryptology and Network Security

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In this paper, we present the first results of the project AMIS (Access Multilingual Information opinionS) funded by Chist-Era. The main goal of this project is to understand the content of a video in a foreign language. In this work, we consider the understanding process, such as the aptitude to capture the most important ideas contained in a media expressed in a foreign language. In other words, the understanding will be approached by the global meaning of the content of a support and not by the meaning of each fragment of a video. Several stumbling points remain before reaching the fixed goal. They concern the following aspects: Video summarization, Speech recognition, Machine translation and Speech segmentation. All these issues will be discussed and the methods used to develop each of these components will be presented. A first implementation is achieved and each component of this system is evaluated on a representative test data. We propose also a protocol for a global subjective evaluation of AMIS.

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“…Components of the IMCOP system dedicated to carry out the above analysis are known as the Metadata Enhancement Services (MES), which, in fact, are REST-compliant Web services in the cloud [2]. Each MES service is intended to perform a single classification task.…”

Section: Integration With Imcop Architecturementioning

confidence: 99%

Software package for measurement of quality indicators working in no-reference model

Nawała

Leszczuk

Zajdel

et al. 2016

Multimed Tools Appl

Self Cite

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The key objective of No-Reference (NR) visual metrics (indicators) is to predict the end-user experience concerning remotely delivered video content. Rapidly increasing demand for easily accessible, high quality video material makes it crucial for service providers to test the user experience without the need for comparison with reference material. In this paper, we present a versatile measurement system and describe various optimisation strategies utilised to reach real-time operation. Furthermore, several calculation automation scripts are described, along with a dedicated graphical user interface, which gives a more comprehensive insight into the presented system. On top of that, we show the results of crowd-sourcing experiments used to estimate subjective threshold values for quality indicators. Additionally, integration with the IMCOP system is introduced.

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The IMCOP System for Data Enrichment and Content Discovery and Delivery

Cited by 11 publications

References 4 publications

Summarizing videos into a target language: Methodology, architectures and evaluation

Summarizing videos into a target language: Methodology, architectures and evaluation

A First Summarization System of a Video in a Target Language

Software package for measurement of quality indicators working in no-reference model

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