Heuristic Video Search Using Semantic Clustering and Feedback
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Solution Overview
Problem
Current video search methods are inefficient for locating and identifying specific video information from large video sources, especially surveillance videos, as they rely on accurate metadata which is not always available, and filtering by object features is time-consuming.
Innovation Solution
A heuristic video search method that performs hierarchical clustering of videos based on semantic features, displays a word cloud to guide user feedback, and generates snapshots of selected video excerpts based on user input, using confidence scores to rank results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hierarchical clustering based on semantic features is implemented, then video search efficiency is improved, but system complexity increases
Solution Approach 1:
The video search system is segmented into multiple hierarchical levels: first-level clustering groups videos by broad semantic categories, second-level clustering further divides these groups into sub-categories, and leaf nodes contain individual videos. This segmentation allows efficient navigation through large video datasets without requiring the entire system to process all videos simultaneously, thus improving search efficiency while managing complexity through structured division.
Solution Approach 2:
The system introduces a hierarchical dimension to the video search space, transforming a flat, unstructured search problem into a multi-level structured problem. By organizing videos across multiple hierarchical levels with different granularities, the system adds a dimensional structure that enables faster navigation and reduces the computational complexity of searching through large video collections.
2Adaptability or versatility
If metadata accuracy is not available, then video identification reliability decreases, but the system must adapt to work with incomplete information
Solution Approach 1:
The system implements feedback mechanisms where user interactions with video snippets and clusters provide continuous information about search relevance. This feedback is used to refine clustering decisions and improve video identification accuracy over time, allowing the system to adapt to incomplete or inaccurate initial metadata while progressively improving reliability through learned patterns from user behavior.
Solution Approach 2:
The system performs preliminary clustering and generates video snippets before user interaction, creating an initial structured organization of videos based on available (even incomplete) metadata. This preliminary action allows the system to present organized results immediately while leaving room for subsequent refinement based on user feedback, thus maintaining adaptability to incomplete information while working toward improved reliability.
3Measurement precision
If filtering by object features is performed, then search precision is improved, but processing time increases
Solution Approach 1:
Object feature filtering is segmented and applied at different hierarchical levels rather than uniformly across all videos. At higher levels, broader semantic features are used for quick grouping, while more detailed object features are applied only within smaller sub-clusters. This segmented approach maintains search precision by applying detailed filtering where needed while reducing overall processing time by avoiding exhaustive feature analysis across the entire video collection.
Solution Approach 2:
The system applies different filtering qualities to different parts of the search space. High-precision object feature filtering is applied locally within smaller clusters where it is most needed, rather than uniformly across all videos. This local quality approach ensures that detailed feature analysis is performed only on relevant subsets of videos, maintaining precision for critical searches while minimizing unnecessary processing time on broader categorization tasks.
Data Source
AI summary
In an approach to improve video searching embodiments execute heuristic video searching based on importance of semantic features. Embodiments perform hierarchical aggregation of a parsed video into one or more clusters of video excerpts based on a plurality of video features, and display a word cloud to a user to guide feedback. Embodiments traverse the one or more clusters based on a data structure of the hierarchical aggregation and the feedback. Additionally, responsive to reaching selected leaf nodes of the data structure, embodiments generate one or more snapshots of the video excerpts associated with the leaf nodes, wherein the leaf nodes are selected based on the feedback. Further, embodiments display the one or more generated snapshots of the video excerpts to the user.