Video Concept Indexing for Cross-Presentation Clustering
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Solution Overview
Problem
Existing video analysis systems struggle to identify and cluster videos based on conceptual similarity due to differences in presentation attributes, leading to inefficient resource utilization and erroneous similarity determinations.
Innovation Solution
A method for generating concept indices from videos using knowledge graphs to determine conceptual similarity, allowing for clustering and efficient distribution of videos based on shared concepts, reducing the need for lengthy learning periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video similarity techniques are used to identify groups of similar videos, then videos with identical presentation attributes can be clustered, but the system fails to identify videos conveying similar concepts through different presentations, leading to erroneous similarity determinations
Solution Approach 1:
The patent extracts the core concept from videos by identifying and removing presentation-specific attributes (format, resolution, duration, etc.). This is achieved through concept indexing that separates the essential semantic content from the variable presentation characteristics, allowing videos with different presentations to be matched based on their underlying concepts alone.
Solution Approach 2:
The patent introduces concept indices as an intermediary representation between raw video content and similarity comparison. These indices serve as a standardized mediator that translates diverse video presentations into a common conceptual framework, enabling accurate similarity determination across different video formats and presentations.
2Productivity
If the system analyzes each video individually to determine distribution strategies, then precise control over video delivery is achieved, but resource utilization becomes inefficient and response times increase
Solution Approach 1:
The patent merges individual video analysis into cluster-level analysis by grouping videos with similar concepts. Once a cluster is formed, the system analyzes aggregate properties of the entire cluster rather than processing each video separately, significantly reducing computational resources and time required for distribution strategy development.
Solution Approach 2:
The patent performs preliminary clustering of videos based on concept indices before conducting detailed analysis. This preliminary organization allows the system to quickly identify which videos belong together conceptually, enabling efficient batch processing and reducing the overall learning period for distribution optimization.
3Manufacturing precision
If the system uses detailed presentation attributes for video comparison, then precise video matching is achieved, but the complexity of the analysis system increases and processing becomes slower
Solution Approach 1:
The patent extracts only the essential conceptual information from videos while discarding unnecessary presentation details. The concept indexing process identifies and retains only those attributes that contribute to conceptual meaning, eliminating complex presentation-specific features that would otherwise increase system complexity without adding value to conceptual matching.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improving content analysis. A method includes obtaining videos uploaded by video publishers. A concept index is generated for each given video. The concept index is generated based on at least (i) a concept conveyed by one or more objects depicted in the video, and (ii) a level of prominence of the concept in the given video. Based on the concept indices of the videos, multiple video groups are created. Each given video group includes two or more different videos that each have a specified level of similarity to other videos in the given video group. Based on data obtained through a feedback loop, insights about the multiple video groups are generated. Distribution of at least one video is modified based on the insights about the given video group that includes the at least one video.