Video Frame Annotation with Entity Probability Scaling
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Solution Overview
Problem
Users face difficulties in identifying relevant video content items among numerous search results due to lacking or inaccurate metadata, making it hard to pinpoint relevant portions of media content.
Innovation Solution
A method for annotating video frames with entities and associated probabilities of existence, using metadata to adjust these probabilities and label frames, thereby improving relevance assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If metadata is provided to help users assess relevance, then users can evaluate video content items, but metadata is often lacking or inaccurate
Solution Approach 1:
The patent introduces an intermediary system (video annotation engine) that bridges the gap between raw video content and user needs. This intermediary automatically generates accurate annotations by analyzing video frames and combining them with metadata, thereby resolving the issue of missing or inaccurate metadata without requiring direct user input for each annotation.
Solution Approach 2:
The system performs self-service by automatically annotating video frames using machine learning models that analyze visual content. The annotation engine processes videos independently, generating relevant annotations without human intervention, thus compensating for incomplete or inaccurate user-provided metadata.
2Productivity
If users search through numerous media content items, then they can find relevant content, but it becomes difficult to assess relevance and pinpoint portions
Solution Approach 1:
The patent segments the video content into individual frames and annotates each frame with relevant entities and probabilities. This segmentation allows users to quickly scan through annotated frames rather than watching entire videos, significantly improving search efficiency and making relevance assessment easier by breaking down complex video content into manageable, labeled units.
Solution Approach 2:
The system uses visual indicators (analogous to color changes) to represent different annotation types and probability levels in search results. By displaying annotated frames with visual markers indicating entity presence and confidence levels, users can rapidly assess relevance without reading detailed metadata, thus improving search efficiency and reducing assessment difficulty.
Data Source
AI summary
A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method selects an entity from a plurality of entities identifying characteristics of a video item, where the video item has associated metadata. The computer-implemented method receives probabilities of existence of the entity in video frames of the video item, and selects a video frame determined to comprise the entity responsive to determining the video frame having a probability of existence of the entity greater than zero. The computer-implemented method determines a scaling factor for the probability of existence of the entity using the metadata of the video item, and determines an adjusted probability of existence of the entity by using the scaling factor to adjust the probability of existence of the entity. The computer-implemented method labels the video frame with the adjusted probability of existence.