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SMPTE Motion Imaging Journal ( Volume: 132, Issue: 3, April 2023)
[ACTIVE]

Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation

Metadata

Publisher
SMPTE — White Plains, NY, USA
Doc Type
Journal Article
Content Type
Original Research
Abbreviated Title
SMPTE Mot. Imag. J
Volume
132, No. 3, pp. 27–38
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.
Publication Date
2023-04-01
DOI
10.5594/JMI.2023.3245685
ISSN
Print: 1545-0279 | Electronic: 2160-2492
Link
https://doi.org/10.5594/JMI.2023.3245685
Author(s)
Rob Gonsalves
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.
Zahra Montajabi
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.
Shailendra Mathur
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.
Nizar Bouguila
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.
Keyword(s)
Artificial Intelligence, CLIP4Clip, Machine Learning, Media Search, Microsoft CLAP, Multilingual CLIP, CLIP, Semantic Search, Wav2CLIP, Wav2vec2
Copyright
© 2023 SMPTE

Bibliographic Reference(s)

  • ML-Based Indexing of Media Libraries for Insights and Search [Active]
  • L. van der Maaten and G. Hinton, “Visualizing Data Using t-SNE,” J. Mach. Learn. Res., 9:2579–2605, 2008. Conclusion Semantic indexing is a new method of searching for and retrieving media files that uses ML to extract and encode semantic representations found in various media formats. This allows for searches using unstructured text queries, which can be faster and more accurate than traditional metadata searches. Semantic representations can also be used to provide insights into the media library. The CLIP model from OpenAI is one example of a model that can be used for semantic indexing, and the Elasticsearch database can be used to store the resulting embeddings. There are many potential applications for semantic search, including finding media with queries that contain summaries or synonyms, using learned cultural knowledge. Additionally, semantic search can be extended to use multiple languages for queries and used to find additional media types like video clips and audio files. Next Steps Recently, the EBU, Movie labs, and SMPTE published a primer29 on using semantic technologies, which, among the various uses, lists contextual search and discovery as one of the benefits. The approach outlined in that primer discusses using a knowledge graph of triples stored in graph databases to relate semantically linked data. This approach is an alternate, if not complementary, approach to semantic search using embeddings. Prior to the primer mentioned earlier, Wei et al.30 provided a comprehensive overview of semantic search systems based on linked data. Semantically linked systems provide for usage that can go beyond conventional search usage. The entity-relationship graph lends itself to queries that go beyond resolving a search query to a digital asset. The entity relationships can be used to discover hitherto unknown relationships between data records and digital assets by traversing edges in the graph to provide insights and recommendations;31 to organize the data in predefined structures, making traversal and automation possible; to connect heterogeneous and distributed data sources while maintaining data integrity. Most of these knowledge graph-based systems require standardized predefined ontologies and a-priori-defined taxonomies to model the relationships. In contrast, text embedding-based systems can discover the taxonomy. Next steps include comparing the architectures and results of using semantic embeddings versus this new approach to knowledge graphs for searching media libraries. Part of the investigation is to evaluate if the technologies complement each other rather than being distinct approaches. EXTERNAL
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Rob Gonsalves, Zahra Montajabi, Shailendra Mathur, and Nizar Bouguila; Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation, SMPTE Motion Imaging Journal ( Volume: 132, Issue: 3, April 2023); SMPTE, 2023. Available at https://doi.org/10.5594/JMI.2023.3245685
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Rob Gonsalves, Zahra Montajabi, Shailendra Mathur, and Nizar Bouguila; Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation, SMPTE Motion Imaging Journal ( Volume: 132, Issue: 3, April 2023); SMPTE, 2023. Available at https://doi.org/10.5594/JMI.2023.3245685

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Rob Gonsalves, Zahra Montajabi, Shailendra Mathur, and Nizar Bouguila; Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation, SMPTE Motion Imaging Journal ( Volume: 132, Issue: 3, April 2023); SMPTE, 2023. Available at https://doi.org/10.5594/JMI.2023.3245685
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<span class="citation">Rob Gonsalves, Zahra Montajabi, Shailendra Mathur, and Nizar Bouguila; <cite>Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation</cite>, SMPTE Motion Imaging Journal ( Volume: 132, Issue: 3, April 2023); SMPTE, 2023. Available at <a href="https://doi.org/10.5594/JMI.2023.3245685" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2023.3245685</a></span>

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Rob Gonsalves, Zahra Montajabi, Shailendra Mathur, and Nizar Bouguila; Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation, SMPTE Motion Imaging Journal ( Volume: 132, Issue: 3, April 2023); SMPTE, 2023
doi: 10.5594/JMI.2023.3245685
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Rob Gonsalves, Zahra Montajabi, Shailendra Mathur, and Nizar Bouguila; <cite id="bib-10-5594-jmi-2023-3245685">Machine Learning Applied to Media Libraries for Insights, Search, and Segmentation</cite>, SMPTE Motion Imaging Journal ( Volume: 132, Issue: 3, April 2023); SMPTE, 2023
<span class="doi">10.5594/JMI.2023.3245685</span>
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