Batch Video Labeling Service with Confidence Scoring
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Solution Overview
Problem
Conventional video content classification systems require users to label videos one-by-one, making the process time-consuming and resource-intensive, especially for large datasets.
Innovation Solution
A machine-powered system architecture that automatically identifies videos of high content similarity and groups them for accelerated labeling using a batch video content classification service within a service provider network. This service employs a video data modeling engine to calculate confidence scores, determining whether videos should be auto-labeled, labeled by human annotators, or presented in single-view or group-view mode for annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users label videos one-by-one in a linear fashion, then labeling accuracy can be maintained, but the time required and computing resources consumed increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically analyzing video content and generating preliminary labels before human annotators review them. The machine learning model processes video data, extracts features, and generates label candidates in advance, so that when human annotators review the videos, they only need to verify or correct the pre-generated labels rather than creating labels from scratch, significantly reducing the time required while maintaining accuracy.
Solution Approach 2:
The system implements feedback mechanisms where human annotators review and correct machine-generated labels, and these corrections feed back into the machine learning model to improve its performance. The model continuously learns from annotated data, refining its labeling capabilities over time, which allows for more accurate automated labeling and reduces the burden on human annotators.
2Device complexity
If conventional linear labeling is used, then simple processing can be maintained, but computing resource utilization and costs increase
Solution Approach 1:
The labeling process is segmented into distinct stages: automated machine learning analysis and preliminary labeling, human annotator review and verification, and model training. By dividing the complex labeling task into these segments, the system can use computing resources more efficiently - the machine learning component handles the computationally intensive initial analysis, while human annotators focus only on verification tasks requiring less computational overhead.
Solution Approach 2:
The machine learning model performs self-service by automatically analyzing video content and generating labels without requiring continuous human intervention. The model processes videos independently, extracts relevant features, and generates label candidates autonomously, significantly reducing the computing resources that would otherwise be required for manual analysis of each video.
3Productivity
If batch processing of similar videos is implemented, then labeling speed increases, but the complexity of the system increases
Solution Approach 1:
The system merges similar videos into batches for simultaneous processing and labeling. By grouping videos with similar characteristics or content into batches, the system can process multiple videos concurrently using the same computational resources and labeling logic, significantly increasing productivity. The machine learning model processes entire batches together, extracting features and generating labels for all videos in the batch simultaneously.
Solution Approach 2:
The system changes processing parameters dynamically based on video characteristics. The machine learning model analyzes video features and adjusts processing parameters such as batch size, label generation thresholds, and annotation priorities according to the specific content and complexity of each video, allowing efficient batch processing while managing system complexity through adaptive parameter adjustment.
Data Source
AI summary
This disclosure describes automatically identifying videos of a video dataset having high-content similarity and grouping the videos together with one or more possible tags for the videos for consideration by annotators. A labeling service of a service provider network receives (i) a dataset of unlabeled videos and (ii) category tags for labeling the videos from a client device associated with a user. A video data modeling engine analyzes the dataset to provide a set of ranked unlabeled videos from the dataset, wherein the set of ranked unlabeled videos are ranked according to confidence scores with respect to at least one tag. Based at least in part on the confidence scores, at least some of the unlabeled videos are labeled with the at least one tag to provide a dataset of labeled videos. The dataset of labeled videos is provided to a database and/or the user.