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

Time shift metric in VQMT tool

    Use PSNR to detect relevant frames.
    Added to the benchmark by MSU G&M Lab

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

ViSiL

    Use RMAC descriptors to estimate frame-to-frame and video-to-video similarity.
    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
22 Oct 2021
See Also
MSU Video Upscalers Benchmark 2022
The most extensive comparison of video super-resolution (VSR) algorithms by subjective quality
MSU HDR Video Reconstruction Benchmark 2022
The most comprehensive comparison of HDR video reconstruction methods
Site structure