List of participants of MSU Video Alignment and Retrieval Benchmark Suite
TMK
- Adapted the kernel descriptor framework of Bo (paper) to sequences of frames. Proposed a query expansion (QE) technique that automatically aligns the videos deemed relevant for the
query.
- Added to the benchmark by MSU G&M Lab
VideoIndexer
- Detect scene changes, split the video on scenes. Align scenes, than align frames respectively.
- Added to the benchmark by MSU G&M Lab
- Links: paper
Time shift metric in VQMT tool
- Use PSNR to detect relevant frames.
- Added to the benchmark by MSU G&M Lab
- Links: project
Time shift metric in VQMT3D tool
- Use motion vectors and RANSAC to measure time shift between frames.
- Added to the benchmark by MSU G&M Lab
- Links: project
ViSiL
- Use RMAC descriptors to estimate frame-to-frame and video-to-video similarity.
![](/assets/img/benchmarks/aligners/ViSiL.png)
- Added to the benchmark by MSU G&M Lab
- This method was modified by MSU to suit the benchmark suite tasks: we measure only frame-to-frame similarity, then we make synchronization map by taking maximum values in the resulting cost matrix.
ViSiL_SCD
- Use ViSiL architecture to compute frames features. Detect scene changes by the features and split videos on scenes. Match the scenes by video-to-video similarity and then make synchronization map by taking maximum values in the frame-to-frame similarity matrix.
- Added to the benchmark by MSU G&M Lab
See Also
Video Colorization Benchmark
Explore the best video colorization algorithms
Defenses for Image Quality Metrics Benchmark
Explore defenses from adv attacks
Learning-Based Image Compression Benchmark
The First extensive comparison of Learned Image Compression algorithms
Super-Resolution Quality Metrics Benchmark
Discover 66 Super-Resolution Quality Metrics and choose the most appropriate for your videos
Video Saliency Prediction Benchmark
Explore the best video saliency prediction (VSP) algorithms
Super-Resolution for Video Compression Benchmark
Learn about the best SR methods for compressed videos and choose the best model to use with your codec
Site structure
-
MSU Benchmark Collection
- Video Colorization Benchmark
- Defenses for Image Quality Metrics Benchmark
- Learning-Based Image Compression Benchmark
- Super-Resolution Quality Metrics Benchmark
- Video Saliency Prediction Benchmark
- Super-Resolution for Video Compression Benchmark
- Metrics Robustness Benchmark
- Video Upscalers Benchmark
- Video Deblurring Benchmark
- Video Frame Interpolation Benchmark
- HDR Video Reconstruction Benchmark
- No-Reference Video Quality Metrics Benchmark
- Full-Reference Video Quality Metrics Benchmark
- Video Alignment and Retrieval Benchmark
- Mobile Video Codecs Benchmark
- Video Super-Resolution Benchmark
- Shot Boundary Detection Benchmark
- The VideoMatting Project
- Video Completion
- Codecs Comparisons & Optimization
- VQMT
- MSU Datasets Collection
- Metrics Research
- Video Quality Measurement Tool 3D
- Video Filters
- Other Projects