Video Retrieval Using Cross-Correlation and Letterbox Cropping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video retrieval techniques face inaccuracies due to differences in frame rates, scene changes, and encoding formats, and struggle with videos having little motion or short durations, leading to inefficient and unreliable similarity matching.
Innovation Solution
A system that extracts features from video signals by cross-correlating mean grayscale values and spatial features, using a combination of cross-correlation analysis and direct bit-wise comparison to determine similarity, and employs a letterbox cropping filter to normalize frames, allowing for robust matching across varying formats and qualities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If key frames are extracted at regular intervals or by scene change detection, then video retrieval can be performed using image retrieval methods, but differences in frame rates and scene changes cause misalignment and reduce matching accuracy
Solution Approach 1:
The patent segments the video into multiple features including color histograms, motion energy vectors, and audio spectra. By dividing the video analysis into multiple independent feature segments, the system can compare videos frame-by-frame using cross-correlation, avoiding the key frame selection problem while maintaining computational feasibility through feature-level processing.
Solution Approach 2:
The patent introduces cross-correlation as an intermediary mathematical operation between two video feature sequences. This intermediary mechanism computes similarity scores for all frame pairs systematically, eliminating the need for heuristic key frame selection and providing accurate temporal alignment even when frame rates differ, thus resolving the contradiction between ease of implementation and matching accuracy.
2Reliability
If temporal modeling is used to model entire video clips, then model-based comparison can be performed during retrieval, but videos with little motion or short durations yield insufficient data for reliable temporal modeling
Solution Approach 1:
The patent changes the parameters used for video representation from temporal models (which require sufficient motion and duration) to multiple complementary feature parameters including color histograms, motion energy vectors, and audio spectra. By computing cross-correlation across these diverse parameters, the system can reliably match videos even when temporal data is insufficient, thus resolving the contradiction between retrieval reliability and available data quantity.
3Adaptability or versatility
If frame rates and encoding formats vary between videos, then video compatibility and adaptability are improved, but existing retrieval techniques produce inaccurate similarity matching
Solution Approach 1:
The patent creates a universal feature extraction framework that computes color histograms, motion energy vectors, and audio spectra from video frames regardless of their original encoding format or frame rate. The cross-correlation operation then systematically compares these normalized features across all frame pairs, providing accurate similarity matching that is invariant to format variations, thus resolving the contradiction between adaptability and matching precision.
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, and finally determining the similarity score based on both the cross-correlation scores.