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  "docId": "10.5594-JMI.2023.3245685",
  "document": {
    "abbrevTitle": "SMPTE Mot. Imag. J",
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "abstract": "Recent advances in machine learning (ML) have produced a new form of semantic indexing that lets users enhance searches and gain new insights into their media libraries. Unlike typical search systems that use extracted metadata, semantic indexing allows users to find relevant material without the need to tag the media with selections from a predefined taxonomy. With semantic search, users can simply enter unstructured text, and the system will find the best matching media clips. This article extends the use of this technology to gather analytics on the data, which can then be further correlated to generate various insights. This new form of media indexing can be performed with the contrastive language-image pretraining (CLIP) model from OpenAI for images and similar models for video and audio. These models encode media into embeddings that can be searched to find the closest semantic similarity, enhanced with learned cultural knowledge. These systems have the benefit of finding media based on keywords, synonyms, and summaries. The systems can also be used for analytics and insights, such as segmentation, shot detection, and creating a 2D map to display correlations. This article ends with a discussion of the next steps, including using knowledge graphs for semantic search.",
    "abstract$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "authors": [
      {
        "bio": "Rob Gonsalves joined Avid Tech?nology as their 15th employee in 1989. He helped to develop the industry’s preeminent nonlinear editing system Avid Media composer, specializing in programming video effects and color correction. His work on multicamera editing software led to Avid winning the Technology and Engineering Emmy Award. He later spearheaded the development of the architecture of Avid’s media enterprise products. He is currently researching the use of artificial intelligence (AI) for media production. He holds over 50 patents in the media and entertainment industry.",
        "name": "Rob Gonsalves",
        "orcid": "0000-0001-5713-5457"
      },
      {
        "bio": "Zahra Montajabi has a BSc degree in computer engineering from the Sharif University of Technology, Tehran, Iran, in 2019, and an MSc degree in quality system engineering from Concordia University, Montreal, QC, Canada, in 2022. She was an AI researcher intern with Avid Technology, Montreal. She is currently an AI/machine learning (ML) software developer with Avid Technology. Her research interests are computer vision, image processing, and ML.",
        "name": "Zahra Montajabi"
      },
      {
        "bio": "Shailendra Mathur is responsible for the technology and architecture of Avid’s products in the role of vice president and chief architect. He has been awarded several patents and presented papers at numerous conferences, panels, and journals on subjects such as AI, computer vision, medical imaging, media creation tools, security, immersive entertainment, asset management, 4K/HDR, high-performance computing, and cloud technologies. With more than 25 years of innovation experience in the media industry, he was privileged to be one of the team members who received the Philo T. Farnsworth Prime Time Engineering Emmy Award on behalf of Avid in 2018.",
        "name": "Shailendra Mathur"
      },
      {
        "bio": "Nizar Bouguila has an engineering degree from the University of Tunis, Tunis, Tunisia, in 2000, and MSc and PhD degrees in computer science from Sherbrooke University, Sherbrooke, QC, Canada, in 2002 and 2006, respectively. He is currently a professor with the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada. His research interests include image processing, ML, data mining, computer vision, pattern recognition, smart buildings, and energy. He is a licensed professional engineer registered in Ontario and a Senior Member of the IEEE for 12 years.",
        "name": "Nizar Bouguila"
      }
    ],
    "authors$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "contentType": "orig-research",
    "contentType$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "copyright": {
      "holder": "SMPTE",
      "holder$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
      },
      "year": "2023",
      "year$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
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    "copyright$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "docId": "10.5594-JMI.2023.3245685",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "docLabel": "SMPTE Motion Imaging Journal ( Volume: 132, Issue: 3, April 2023)",
    "docLabel$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "docTitle": "Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation",
    "docTitle$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "docType": "Journal Article",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
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    "doi": "10.5594/JMI.2023.3245685",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "href": "https://doi.org/10.5594/JMI.2023.3245685",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "issn": {
      "electronic": "2160-2492",
      "electronic$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
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      "print": "1545-0279",
      "print$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
        "source": "parsed",
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    "issn$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "journalAcronym": "MIJR",
    "journalAcronym$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "journalTitle": "SMPTE Motion Imaging Journal",
    "journalTitle$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "keywords": [
      "Artificial Intelligence",
      "CLIP4Clip",
      "Machine Learning",
      "Media Search",
      "Microsoft CLAP",
      "Multilingual CLIP",
      "CLIP",
      "Semantic Search",
      "Wav2CLIP",
      "Wav2vec2"
    ],
    "keywords$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "number": "3",
    "number$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "pages": "27–38",
    "pages$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
      "source": "parsed",
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    "publicationDate": "2023-04-01",
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      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "publisher": "SMPTE",
    "publisher$meta": {
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    "publisherLocation": {
      "city": "White Plains, NY",
      "city$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
        "source": "parsed",
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        "version": "smpte-idams-publication@v1"
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      "country": "USA",
      "country$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2023/7261654/10089985/10090227.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
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      "source": "parsed",
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    "references": {
      "bibliographic": [
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}