Video Summarization via Shot Clustering and Saliency Tuning
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Solution Overview
Problem
Current video summarization methods fail to effectively condense large video collections into concise summaries that preserve the structural integrity and semantic meaning of the original content, often losing video structure and introducing information gaps due to uneven statistical distributions of video segments.
Innovation Solution
A method and system that divide videos into shots, analyze frame saliency, extract key frames, and perform shot clustering based on visual and audio features, using a saliency tuning method to fuse visual and audio saliency values and generate summaries through constrained optimization, ensuring maximum saliency accumulation and uniform distribution across the video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If uniform sampling or time compression methods are used to shrink video size, then video length is reduced, but semantic meaning and structural integrity are lost
Solution Approach 1:
The patent replaces mechanical time compression (uniform sampling, frame dropping) with a semantic-based selection system that uses shot clustering, saliency detection, and concept pattern recognition to intelligently choose representative shots, thereby preserving semantic meaning while reducing video length
Solution Approach 2:
The patent changes the selection criteria from uniform temporal sampling to multi-parameter optimization based on shot saliency, audio-visual correlation, and concept pattern diversity, transforming the summarization process from mechanical compression to semantic-aware selection
2Loss of information
If frame-level skimming based on user attention model is used, then interesting parts are identified, but video structure is not maintained
Solution Approach 1:
The patent segments the video at the shot level rather than frame level, using shot boundary detection to identify natural structural units. This segmentation approach maintains video structure by preserving complete shots with their inherent temporal and semantic coherence, while still enabling selective summarization of interesting content through shot clustering
Solution Approach 2:
The patent introduces shot clustering and concept pattern recognition as intermediary layers between frame-level saliency detection and final summary generation. These intermediaries bridge the gap by grouping salient frames into structured shots and organizing shots into concept patterns, thereby maintaining video structure while capturing interesting content
3Device complexity
If shot clustering is used for video abstraction, then video is organized into groups, but readability is mostly ignored
Solution Approach 1:
The patent applies local quality by ensuring that each shot cluster represents a distinct concept pattern with high internal coherence and external distinguishability. By optimizing cluster quality at the local level (individual shot selection within clusters) and global level (distribution across concept patterns), the patent achieves both organized structure and high readability in the summary
4Loss of information
If semantic level skimming is attempted, then understanding of video content is improved, but implementation is difficult due to semantic gap
Solution Approach 1:
The patent introduces concept patterns as intermediary representations that bridge the semantic gap between low-level visual features and high-level content understanding. Concept patterns serve as abstracted concept descriptors that capture semantic meaning without requiring full semantic interpretation, making implementation feasible while improving content understanding
Solution Approach 2:
The patent replaces the need for complex semantic interpretation with a feature-based approach using shot clustering and concept pattern recognition. By substituting semantic gap bridging with statistical pattern recognition and saliency-based selection, the patent achieves content understanding without the implementation difficulties of true semantic analysis
Data Source
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AI summary
A video summary method comprises dividing a video (202) into a plurality of video shots (206), analyzing each frame in a video shot (206) from the plurality of video shots (206), determining a saliency of each frame of the video shot, determining a key frame (210) of the video shot (206) based on the saliency of each frame of the video shot (206), extracting visual features from the key frame (210) and performing shot clustering of the plurality of video shots (206) to determine concept patterns based on the visual features. The method further comprises fusing different concept patterns using a saliency tuning method and generating a summary of the video based upon a global optimization method.