Representative Frame Selection via Importance and Evaluation Metrics
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Solution Overview
Problem
Current systems lack an effective method to select representative frames from video information that capture the essence of a video sequence, balancing similarity and evaluation criteria to provide a concise summary.
Innovation Solution
A representative frame selecting system that calculates the importance of frames based on similar frame intervals and evaluation values from adjacent frames, using a weight calculation to identify local maximum integrated evaluation frames for selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frames are selected based solely on similarity metrics, then frames capturing identical visual content are selected, but frames that provide diverse and informative summary are lost
Solution Approach 1:
The patent applies local quality by evaluating each frame's importance individually through multiple metrics (similarity to other frames, temporal position, visual characteristics) rather than using a single global criterion. This allows different frames to be selected based on their specific local properties, ensuring both accuracy and diversity in the representative frame set.
Solution Approach 2:
The patent changes the selection parameters from simple similarity thresholds to a composite scoring system that includes similarity metrics, temporal information, and visual importance weights. This parameter transformation enables the system to balance between selecting identical frames and preserving diverse content representations.
2Loss of information
If multiple frames are selected to capture comprehensive content, then video summary completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating frame importance scores and similarity metrics before the actual frame selection process. This preliminary computation allows the system to quickly identify and select representative frames without performing exhaustive analysis during the selection phase, significantly reducing processing time while maintaining completeness.
Solution Approach 2:
The patent applies partial action by selecting only the necessary number of representative frames based on importance thresholds rather than processing all frames equally. This selective approach maintains comprehensive content representation while reducing computational overhead and processing time.
3Stability of the object's composition
If frames are selected based on temporal position, then chronological order is maintained, but visually important frames may be missed
Solution Approach 1:
The patent merges multiple selection criteria including temporal position, similarity metrics, and visual importance into a unified frame selection process. This combination allows the system to maintain temporal sequence integrity while simultaneously identifying visually important frames, as all criteria are integrated rather than applied sequentially.
Data Source
AI summary
A representative frame selecting system includes: an importance obtaining unit that obtains importance of each frame from calculating on the basis of a length of a similar frame interval formed of consecutive frames each of which has a value according to a similarity between each of the frames included in video information that is equal to or more than a standard; an evaluation value obtaining unit that obtains an evaluation value of each frame from calculating on the basis of an evaluation standard from frames adjacent to each frame; and a representative frame selecting unit that selects at least one representative frame among the frames included in the video information, on the basis of the importance and the evaluation value of each frame.