Visual Relevance Ranking for Video Search Refinement
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Solution Overview
Problem
Current search methods for video and multimedia content rely on Boolean operators and fail to effectively identify content based on the importance of characteristics, leading to unsatisfactory search results and user interface limitations.
Innovation Solution
A robust search method that considers the importance of tags attached to content, using visual relevance ranking to group and display videos based on salient objects and scenes, providing users with thumbnails that link to relevant content and allowing for interactive refinement of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Boolean operators are used for searching video content, then the search system is simple and easy to operate, but the search accuracy and ability to identify content based on importance is insufficient
Solution Approach 1:
The patent changes the search parameters from simple Boolean operators to visual relevance ranking based on tag importance weights. The system assigns different weights to different tags (e.g., object type, color, size) and ranks video frames based on the visual relevance of matching tags, enabling more accurate content identification while maintaining user-friendly operation through automatic relevance assessment
2Ease of operation
If all search results are displayed equally, then the user interface is simple, but the user cannot easily identify and access the most relevant content
Solution Approach 1:
The patent applies local quality by differentiating the display of search results based on their relevance. The most relevant video frames or clips are highlighted or emphasized in the search results interface, while less relevant results are displayed with lower prominence. This allows users to quickly identify and access the most relevant content without being overwhelmed by all results equally
3Measurement precision
If video content is searched without visual analysis, then the processing time is short, but the search cannot identify content based on visual characteristics and importance
Solution Approach 1:
The patent performs preliminary visual analysis and tag extraction during the indexing phase, where video content is pre-processing to identify objects, scenes, and visual characteristics. This preliminary action stores visual information and relevance data in advance, so that during actual search operations, the system can quickly retrieve and rank results based on pre-computed visual relevance without performing time-consuming analysis in real-time
Data Source
AI summary
A method analyzes the visual content of media such as videos for collecting together visually-similar appearances in their constituent images (e.g. same scenes, same objects, faces of the same people.) As a result, the most relevant and salient (of clearest and largest presence) visual appearances depicted in the videos are presented to the user, both for the sake of summarizing the video content for the users to “see before they watch” (that is, judge by the depicted video content in a filmstrip-like summary whether they want to mouse-click on the video and actually spend time watching it), as well as for allowing to users to further refine their video search result set according to the most relevant and salient video content returned (e.g. largest screen-time faces).