Reverse Video Search Large-scale Media Collections | Presentation@Q3-AIDI
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Reverse video search is the problem of retrieving all videos that share the same content with a given query from a video database. We tackle this problem by building a novel system that contains two main components: i) a video similarity learning method for the accurate similarity calculation, and ii) an efficient indexing scheme for fast retrieval. We present evaluation results and a case study on the problems of video verification and copyright management.
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Video processing involves manipulating and analyzing digital video sequences. Common techniques include trimming, resizing, adjusting brightness/contrast, and analyzing using machine learning. Key concepts in video include compression, frames, frame rate, resolution, and aspect ratio. Compression reduces file sizes while maintaining quality. Frames are still images that make up video sequences. Frame rate determines smoothness. Resolution is pixels and quality. Aspect ratio is width to height ratio. Video can be compressed using intra-frame or inter-frame techniques. Enhancement improves quality using techniques like noise reduction and color correction. Analysis extracts information from video.
Key Frame Extraction in Video Stream using Two Stage Method with Colour and S...
Key Frame Extraction is the summarization of videos for different applications like video object recognition and classification, video retrieval and archival and surveillance is an active research area in computer vision. In this paper describe a new criterion for well presentative key frames and correspondingly, create a key frame selection algorithm based Two stage Method. A two stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Khaing Thazin Min | Wit Yee Swe | Yi Yi Aung | Khin Chan Myae Zin "Key Frame Extraction in Video Stream using Two-Stage Method with Colour and Structure" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27971.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-processing/27971/key-frame-extraction-in-video-stream-using-two-stage-method-with-colour-and-structure/khaing-thazin-min
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Reverse Video Search Large-scale Media Collections | Presentation@Q3-AIDI
- 1. Reverse Video Search on Large-scale Media Collections MeVer Giorgos Kordopatis-Zilos
- 2. Reverse Video Search Query video Retrieved videos Video database
- 3. Video-level methods Limited retrieval performance Very fast retrieval time
- 4. Frame-level methods Slow retrieval time High retrieval performance
- 5. Aim & solution Our Aim • Development of a system for reverse video search • Very high retrieval performance • Fast retrieval speed Our Solution • Two main components • Video indexing & filtering • Video-level method • Efficient video indexing and filtering • Video similarity calculation • Frame-level method • Video similarity learning
- 6. Video indexing & filtering (1/2) Layer Bag-of-Word (LBoW) • Extract a number of L visual words from each video frame • Index videos based on the extracted words Kordopatis-Zilos et al. “Near-Duplicate Video Retrieval by Aggregating Intermediate CNN Layers”. MMM, 2017.
- 7. Video indexing & filtering (2/2) Similarity calculation • Video-level representations with tf-idf weighting • Cosine similarity Video filtering • Rank videos based on their similarity • Select the top N videos (set to N = 5,000) • Select videos with similarity greater than t Kordopatis-Zilos et al. “Near-Duplicate Video Retrieval by Aggregating Intermediate CNN Layers”. MMM, 2017.
- 8. Video similarity calculation (1/2) Video Similarity Learning (ViSiL) • Learn a video similarity function that considers: • Spatial structure of video frames (intra-frame relations) • Temporal structure of videos (inter-frame relations) Kordopatis-Zilos et al. “ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning”. ICCV, 2019.
- 9. Video similarity calculation (2/2) Video Similarity Learning network • 4-layer CNN • Captures the temporal structures in the similarity matrix Kordopatis-Zilos et al. “ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning”. ICCV, 2019.
- 10. Experimental setup FIVR-200K dataset • 225,960 videos from 4,687 news events • 100 query videos • Three retrieval tasks • Simulate different scenarios Evaluation metrics • mean Average Precision (mAP) Kordopatis-Zilos et al. “FIVR: Fine-grained Incident Video Retrieval”. IEEE TMM, 2019.
- 11. Video examples Query Video Complementary Scene Video Duplicate Scene Video Incident Scene Video Kordopatis-Zilos et al. “FIVR: Fine-grained Incident Video Retrieval”. IEEE TMM, 2019.
- 12. Experiments
- 13. Experiments
- 14. Experiments
- 15. Experiments
- 16. Experiments Kordopatis-Zilos et al. “ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning”. ICCV, 2019. Additional experimental results • Evaluation on four video retrieval problems • Achieves state-of-the-art performance
- 17. Video verification query video database video frame-to-frame similarity matrix ViSiL output video similarity 0.8 0.5 near-duplicate videos same event videos
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- 19. Copyright management (2/2) original video reaction video ViSiL output video similarity 0.84
- 20. Online demo
- 21. Tips • Video-level methods offer a fast video retrieval solution but with limited performance • Frame-level methods achieve high retrieval performance, but with very high computation cost • Combination of the two method types with a carefully selected similarity threshold according to the application scenario
- 22. Thank you! Get in touch: Giorgos Kordopatis-Zilos: georgekordopatis@iti.gr / @g_kordo Team info: https://mever.iti.gr/ https://twitter.com/meverteam Code & models: https://github.com/MKLab-ITI/FIVR-200K https://github.com/MKLab-ITI/visil With the support of: MeVer