A systematic review on content-based video retrieval

Spolaôr, Newton; Lee, Huei; Takaki, Weber Shoity Resende; Ensina, Leandro Augusto; Coy, Cláudio Saddy Rodrigues; Wu, Feng

doi:10.1016/j.engappai.2020.103557

Cited by 59 publications

(18 citation statements)

References 71 publications

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“…The HOG descriptor is then used to compute the edge-oriented histogram for each cell at every pyramidal level. Spolaôr et al [36] proposed a systematic review of the relevant literature on CBVR techniques. Especially, the techniques of segmentation, reduction of dimensions, and extraction of low-level visual features.…”

Section: Low-level Visual Feature Extractionmentioning

confidence: 99%

A distributed Content-Based Video Retrieval system for large datasets

Saoudi

Andaloussi

2021

J Big Data

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With the rapid growth in the amount of video data, efficient video indexing and retrieval methods have become one of the most critical challenges in multimedia management. For this purpose, Content-Based Video Retrieval (CBVR) is nowadays an active area of research. In this article, a CBVR system providing similar videos from a large multimedia dataset based on query video has been proposed. This approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key frames for rapid browsing and efficient video indexing. The proposed method has been implemented on both single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments were performed using various benchmark action and activity recognition datasets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to previous studies.

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Section: Low-level Visual Feature Extractionmentioning

confidence: 99%

A distributed Content-Based Video Retrieval system for large datasets

Saoudi

Andaloussi

2021

J Big Data

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“…High definition video compression, video summarization can be performed using video Object Segmentation [22] and Video object Tracking mechanism. [61,64] categorized content based video indexing and retrieval as four concepts such as; Video segmentation, indexing, dimension decrease and machine learning (ML)techniques. Segmentation of video into shots, the fundamental unit is performed to extract the necessary information describing the frames and shot boundary detection [46].…”

Section: Video Retrievalmentioning

confidence: 99%

State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

Megala¹

2021

TURCOMAT

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Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standards, describes the main concepts involved in video coding. Video compression performs conversion of large raw bits of video sequence into a small compact one, achieving high compression ratio with good video perceptual quality. Removing redundant information is the main task in the video sequence compression. A survey on various block matching algorithms, quantization and entropy coding are focused. It is found that many of the methods having computational complexities needs improvement with optimization.

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“…A presente revisão sistemática da literatura utilizou o procedimento proposto por Kitchenham (2004) para o mapeamento do estado da arte sobre a aceitação de casas inteligente por parte dos clientes. O procedimento proposto por Kitchenham (2004) foi inicialmente desenvolvido na área de Engenharia de Sistemas, mas apresenta aplicação disseminada em outras áreas da engenharia, tais como gestão de projetos (e.g., MUSAWIR; ABD-KARIM; MOHD-DANURI, 2020), engenharia industrial (e.g., FETTERMANN, Diego De Castro; ECHEVESTE, 2014; KANG; CATAL; TEKINERDOGAN, 2020; SILVA; ANDRADE; FERREIRA, 2020) e inteligência artificial (e.g., SPOLAÔR et al, 2020). O processo de revisão sistemática proposto por Kitchenham (2004) recomenda três principais fases: planejamento da revisão, condução da revisão e relato da revisão.…”

Section: Método De Pesquisaunclassified

Quais os fatores que interferem na aceitação das tecnologias da casa inteligente pelo usuário: uma revisão

Nascimento

Fettermann

2021

P&P

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O avanço das tecnologias para automação residencial possibilitou o desenvolvimento das casas inteligentes, sendo que os sistemas de casas inteligente tem sido discutido nas áreas de tecnologia, política e sustentabilidade. Porém, o sucesso de implementação da casa inteligente depende de diversos fatores de aceitação dessa tecnologia por parte dos usuários. Assim, o objetivo deste trabalho é identificar na literatura os fatores que possam ser determinantes no processo de adoção da casa inteligente. Foi utilizada a abordagem de revisão sistemática da literatura. Foram identificados quatro grupos de benefícios promovidos pela casa inteligente: saúde, ambiental, financeiro e psicológico, além de três grandes grupos de barreiras que podem afetar a adoção da casa inteligente: tecnológicas; financeira, ética e legal; e falta de conhecimento e resistência psicológica. Os resultados possibilitam definições de melhores estratégias para a implementação e aceitação desta tecnologia por parte dos usuários.

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A systematic review on content-based video retrieval

Cited by 59 publications

References 71 publications

A distributed Content-Based Video Retrieval system for large datasets

A distributed Content-Based Video Retrieval system for large datasets

State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

Quais os fatores que interferem na aceitação das tecnologias da casa inteligente pelo usuário: uma revisão

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