Video Analysis Using Near-Duplicate Keyframe Indexing
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Solution Overview
Problem
Current video search engines face challenges in accurately retrieving and classifying videos due to the reliance on textual metadata, which often lacks precision, and struggle to identify near-duplicate keyframes within large video corpora, leading to difficulties in finding related videos and summarizing content effectively.
Innovation Solution
A system and method that segment videos into keyframes, represent near-duplicate keyframes as indices, and render them in a graphical representation to determine relationships between video content, employing an analogy between video genetics to classify, analyze, and visualize videos, allowing for improved precision and efficiency in video retrieval and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video search engines rely on textual metadata for video retrieval, then the system complexity remains low, but the precision of video retrieval deteriorates
Solution Approach 1:
The patent segments videos into keyframes and further segments keyframes into superpixels, creating a hierarchical structure that enables precise content analysis without requiring complex overall video processing. This segmentation allows the system to focus on representative visual elements rather than processing entire videos or relying solely on metadata.
Solution Approach 2:
The patent replaces traditional text-based metadata search mechanisms with visual content-based analysis using color histograms and superpixel representations. This substitution enables the system to directly analyze video content visually rather than relying on textual descriptions, significantly improving retrieval precision.
2Measurement precision
If the system analyzes large video corpora to find near-duplicate keyframes, then the classification accuracy improves, but the processing time increases
Solution Approach 1:
By segmenting videos into keyframes and then into superpixels, the system reduces the computational burden of analyzing entire videos. This hierarchical segmentation allows for efficient comparison of visual content while maintaining high classification accuracy through detailed superpixel-level analysis of representative frames.
Solution Approach 2:
The patent transforms video data into color histogram representations and superpixel features, changing the parameter space from raw video pixels to compressed visual descriptors. This parameter transformation enables efficient comparison and classification of near-duplicate keyframes while maintaining accuracy.
3Loss of information
If the system renders near-duplicate keyframes in graphical representation to determine relationships, then the visualization quality improves, but the computational resources required increase
Solution Approach 1:
The patent extracts near-duplicate keyframes from large video corpora and renders only these representative frames in graphical representations. This extraction approach allows the system to visualize relationship information completely while consuming fewer computational resources by focusing on a subset of key frames rather than processing entire video collections.
Data Source
AI summary
A system and method for analyzing video include segmenting video stored in computer readable storage media into keyframes. Near-duplicate keyframes are represented as a sequence of indices. The near-duplicate keyframes are rendered in a graphical representation to determine relationships between video content.