Video Segment Clustering for User-Intent Boundary Positioning
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Solution Overview
Problem
Conventional video search methods relying on basic keyword tagging or content analysis are insufficient for accurately identifying similar video content based on user queries, particularly in noisy environments or when users are interested in specific features.
Innovation Solution
A method involving user-driven video segment clustering and boundary positioning, where user inputs are processed to identify intentions, key frames are filtered and clustered, and additional frames are selected based on similarity thresholds, generating a cohesive video clip aligned with user intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword tagging or content analysis methods are used, then the system is simple and easy to implement, but the accuracy of video content search is insufficient
Solution Approach 1:
The patent segments videos into key frames and non-key frames, then processes them separately. Key frames are extracted and indexed, while non-key frames are processed in batches. This segmentation enables more accurate content analysis without overwhelming system complexity by breaking down the processing task into manageable components.
Solution Approach 2:
The patent performs preliminary actions by pre-extracting and indexing key frames before actual search queries are executed. This pre-processing creates an efficient index structure that can quickly retrieve relevant video segments during user queries, improving search accuracy without adding complexity during the actual search operation.
2Productivity
If all video frames are processed to ensure comprehensive search results, then search completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent divides video frames into key frames and non-key frames, processing only key frames for indexing and immediate retrieval. Non-key frames are processed in batches only when necessary, significantly reducing processing time while maintaining search completeness through the key frame representation.
Solution Approach 2:
The patent extracts only the essential key frames from complete video sequences for indexing and search operations. By taking out only the necessary key information rather than processing all frames, the system achieves high search efficiency and speed while maintaining result completeness through the extracted key frame representations.
3Measurement precision
If basic keyword tagging is used, then the system is simple and fast, but it cannot accurately identify similar video content in noisy environments or for specific feature searches
Solution Approach 1:
The patent applies local quality analysis by examining specific regions and features within video frames rather than treating the entire video uniformly. Key frames are identified based on local feature characteristics, and similarity search focuses on these specific high-quality regions, enabling accurate identification in noisy environments without processing the entire video complexity.
Solution Approach 2:
The patent performs preliminary extraction and indexing of key frames with their associated features before actual search queries. This pre-processing identifies and prepares the most representative video segments in advance, enabling high-precision search results for specific features or noisy environments without adding complexity during the actual query execution.
Data Source
AI summary
Method and apparatus for video processing. A plurality of videos are accessed. An index is generated based on one or more key frames and one or more video features extracted from the plurality of video. A user input is processed to identify user intentions. The index is filtered to identify a plurality of candidate key frames based on the user intentions. A recommended video is generated based on the plurality of candidate key frames, further comprising selecting one or more additional frames based on processing the plurality of candidate key frames using one or more clustering operations, and aggregating the plurality of candidate key frames and the one or more additional frames.