Video Retrieval Using Cross-Correlation and Mean Magnitude Difference
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Solution Overview
Problem
Existing video retrieval techniques face inaccuracies due to differences in frames per second, scene change detection errors, difficulty in aligning video data in the time domain, challenges with videos having little motion, and inefficiencies with short videos, especially those with varying formats and encoding schemes.
Innovation Solution
The system extracts features from video signals, cross-correlates them, calculates cross-correlation and mean magnitude difference scores, and combines these scores to determine similarity, using standard deviation, mean pixel values, and color differences, while accounting for temporal and spatial features, including a letterbox cropping filter and resizing to ensure accurate alignment and feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If key frames are extracted at regular intervals for video retrieval, then the retrieval process is simplified, but alignment accuracy deteriorates due to differences in frames per second between videos
Solution Approach 1:
The patent replaces the mechanical approach of extracting key frames at regular intervals with a signal processing approach. It converts video frames into time-series signals and uses cross-correlation analysis to find temporal alignments, substituting direct frame comparison with spectral domain analysis that is invariant to frame rate differences.
Solution Approach 2:
The patent transforms the video retrieval problem from spatial domain (comparing image frames) to temporal frequency domain (comparing time-series signals). By converting frames to signals and analyzing them in the frequency domain through cross-correlation, it achieves alignment that is robust to frame rate variations.
2Loss of information
If scene change detection algorithms are used to select key frames, then content representation is improved, but reliability deteriorates due to detection errors and propagation of error
Solution Approach 1:
The patent extracts the essential temporal structure from videos by converting frame sequences into time-series signals that capture content variations. Instead of relying on scene change detection to select specific frames, it extracts continuous temporal signatures that represent the entire video content, eliminating the need for discrete key frame selection.
Solution Approach 2:
The patent changes the parameter space from discrete key frame selections to continuous time-series signal representations. By transforming video content into temporal signals and using cross-correlation to compare these signals, it achieves robust content-based retrieval that is insensitive to the specific frames selected or frame rate variations.
3Reliability
If temporal modeling is used for video retrieval, then motion information is captured, but difficulty in aligning video data in the time domain increases
Solution Approach 1:
The patent substitutes direct temporal domain alignment with frequency domain analysis. Instead of attempting to align videos frame-by-frame in the time domain, it converts temporal variations into spectral representations and uses cross-correlation to determine alignment, greatly simplifying the temporal alignment problem.
Solution Approach 2:
The patent moves the alignment problem from the time domain to the frequency domain. By representing video temporal structure as spectral signatures and using cross-correlation in the frequency domain, it achieves accurate temporal alignment without the complexity of direct time-domain comparison.
4Measurement precision
If cross-correlation analysis is applied to video signals, then alignment accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent implements cross-correlation analysis through efficient signal processing techniques. By converting video frames to compact time-series signals rather than full image frames, and using Fast Fourier Transform-based cross-correlation, it achieves accurate alignment while reducing computational complexity compared to direct frame-by-frame comparison.
Data Source
AI summary
Techniques for determining if two video signals match by extracting features from a first and second video signal, and cross-correlating the features thereby providing a cross-correlation score at each of a number of time lags, then determining a mean magnitude of a difference of average values at each of the lags, and finally determining the similarity score based on both the cross-correlation scores and the mean magnitude difference scores, and optionally then outputting an indication of a degree of match between the first and second video signals.