Visual Fingerprint Video Search Catalog
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Solution Overview
Problem
Current video search methods rely on textual metadata and Boolean operators, which are ineffective in accurately representing and retrieving video or audio content, leading to poor performance in identifying and retrieving video content.
Innovation Solution
A video search and identification system that uses a catalog to represent relationships between video, data, and objects, enabling querying and search based on visual representations, including video nodes, metadata nodes, and card nodes, with visual fingerprints generated from frames to match against a base set of signatures, and confidence measures to adjust query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual-based fingerprint matching is used, then search accuracy is improved, but device complexity increases
Solution Approach 1:
The video frame is divided into multiple cells to generate visual fingerprints. Each cell's brightness is independently analyzed to create bit representations, allowing complex visual data to be segmented into manageable units for processing and comparison.
Solution Approach 2:
Visual fingerprints are created as simplified representations (copies) of the actual video frames. These fingerprints capture essential visual characteristics through bit patterns and confidence measures, enabling search operations without requiring processing of the full original video data.
2Measurement precision
If visual fingerprints and confidence measures are processed, then retrieval precision is improved, but processing time increases
Solution Approach 1:
Different cells in the visual fingerprint are assigned confidence measures based on their local quality and reliability. This allows the system to weight certain features more heavily than others during matching, improving precision while managing processing requirements by focusing computational effort where it matters most.
Solution Approach 2:
The system transforms continuous visual data into discrete bit representations with associated confidence parameters. This parameter transformation enables efficient comparison operations while maintaining the essential information needed for precise retrieval.
3Productivity
If visual-based matching is implemented, then search effectiveness is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system replaces complex visual analysis mechanisms with a simplified bit-based representation system. By converting visual frame data into binary fingerprints with confidence measures, the complexity of visual comparison is substituted with straightforward bit-matching operations that are easier to implement and measure.
Data Source
AI summary
Systems and methods for identifying and searching video are disclosed. A video search and identification system includes a catalog representing relationships between video, data and/or objects to enable querying and search based on visual representations of video as well as data or other information associated with the video. In one example, the catalog includes video nodes, metadata nodes and card nodes, although additional or fewer node types may be used. A visual-based video identification system is provided to identify content in video sources. An unidentified video source is accessed and visual fingerprints of one or more frames are generated as query signatures for matching against a base set of known signatures. Confidence measures are generated at the bit level to assist in query signature processing.