{"title":"Bilingual video captioning model for enhanced video retrieval","authors":"","doi":"10.1186/s40537-024-00878-w","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Many video platforms rely on the descriptions that uploaders provide for video retrieval. However, this reliance may cause inaccuracies. Although deep learning-based video captioning can resolve this problem, it has some limitations: (1) traditional keyframe extraction techniques do not consider video length/content, resulting in low accuracy, high storage requirements, and long processing times; (2) Arabic language support in video captioning is not extensive. This study proposes a new video captioning approach that uses an efficient keyframe extraction method and supports both Arabic and English. The proposed keyframe extraction technique uses time- and content-based approaches for better quality captions, fewer storage space requirements, and faster processing. The English and Arabic models use a sequence-to-sequence framework with long short-term memory in both the encoder and decoder. Both models were evaluated on caption quality using four metrics: bilingual evaluation understudy (BLEU), metric for evaluation of translation with explicit ORdering (METEOR), recall-oriented understudy of gisting evaluation (ROUGE-L), and consensus-based image description evaluation (CIDE-r). They were also evaluated using cosine similarity to determine their suitability for video retrieval. The results demonstrated that the English model performed better with regards to caption quality and video retrieval. In terms of BLEU, METEOR, ROUGE-L, and CIDE-r, the English model scored 47.18, 30.46, 62.07, and 59.98, respectively, whereas the Arabic model scored 21.65, 36.30, 44.897, and 45.52, respectively. According to the video retrieval, the English and Arabic models successfully retrieved 67% and 40% of the videos, respectively, with 20% similarity. These models have potential applications in storytelling, sports commentaries, and video surveillance.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"11 1","pages":""},"PeriodicalIF":8.6000,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1186/s40537-024-00878-w","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Many video platforms rely on the descriptions that uploaders provide for video retrieval. However, this reliance may cause inaccuracies. Although deep learning-based video captioning can resolve this problem, it has some limitations: (1) traditional keyframe extraction techniques do not consider video length/content, resulting in low accuracy, high storage requirements, and long processing times; (2) Arabic language support in video captioning is not extensive. This study proposes a new video captioning approach that uses an efficient keyframe extraction method and supports both Arabic and English. The proposed keyframe extraction technique uses time- and content-based approaches for better quality captions, fewer storage space requirements, and faster processing. The English and Arabic models use a sequence-to-sequence framework with long short-term memory in both the encoder and decoder. Both models were evaluated on caption quality using four metrics: bilingual evaluation understudy (BLEU), metric for evaluation of translation with explicit ORdering (METEOR), recall-oriented understudy of gisting evaluation (ROUGE-L), and consensus-based image description evaluation (CIDE-r). They were also evaluated using cosine similarity to determine their suitability for video retrieval. The results demonstrated that the English model performed better with regards to caption quality and video retrieval. In terms of BLEU, METEOR, ROUGE-L, and CIDE-r, the English model scored 47.18, 30.46, 62.07, and 59.98, respectively, whereas the Arabic model scored 21.65, 36.30, 44.897, and 45.52, respectively. According to the video retrieval, the English and Arabic models successfully retrieved 67% and 40% of the videos, respectively, with 20% similarity. These models have potential applications in storytelling, sports commentaries, and video surveillance.
期刊介绍：
The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
