AI Self-Labelling With Image Vectors and Incremental Learning
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Solution Overview
Problem
Conventional manual labelling of multimedia content for AI training is labor-intensive, time-consuming, prone to errors, and inadequate for real-time analysis and adapting to new environments, especially in dynamic applications like security surveillance and object detection.
Innovation Solution
An AI-based self-labelling method that creates image vectors from multimedia content, assigns dimensions (frequency, recency, and pattern), determines relevant labels, and allows user input for new labels, enabling incremental learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labelling is used for AI training, then labelling accuracy can be maintained through human judgment, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system enables self-labelling by allowing the AI model to automatically generate labels for multimedia content based on its own analysis and understanding. The model processes images, videos, or audio files and assigns labels autonomously without requiring manual human annotation, thus achieving both high productivity and maintained accuracy through the model's learned patterns
Solution Approach 2:
The patent replaces the mechanical human labelling process with an automated AI-based system. Instead of human annotators manually examining and labelling content, the system uses machine learning models to perform the labelling function, substituting human cognitive effort with computational processes that can scale efficiently
2Adaptability or versatility
If manual labelling is used, then flexibility in handling diverse content types is maintained, but the system cannot adapt rapidly to new environments and unfamiliar objects
Solution Approach 1:
The system performs preliminary learning by training the AI model on diverse multimedia content before deployment. The model pre-learns patterns, objects, and environments, enabling it to rapidly adapt to new situations without requiring time-consuming manual relabelling when encountering new threats or environments
Solution Approach 2:
The labelling system is designed to be dynamic and adaptive rather than static. The AI model can update its understanding and labelling criteria based on new data and environments, allowing the system to evolve and adapt to changing conditions without being constrained by fixed manual labelling protocols
3Productivity
If conventional AI algorithms are used for automated labelling, then processing speed is improved, but the system struggles with novel threats and unpredictable variables in open environments
Solution Approach 1:
The system incorporates feedback mechanisms where the AI model's labelling performance is continuously evaluated and used to improve future labelling. The model learns from its predictions and adjustments, refining its ability to handle novel threats and unpredictable variables while maintaining high processing speed through automated operations
Solution Approach 2:
The patent employs parameter changes by adjusting the AI model's configuration, training data, and algorithmic parameters to optimize performance for different types of content and threats. This allows the system to maintain high speed while adapting to novel situations by modifying its operational parameters rather than requiring complete retraining
Data Source
AI summary
The disclosure relates to an Artificial Intelligence (AI) based self-labelling method and system. The AI based self-labelling method includes creating, in real-time, image vectors from multimedia content captured via a camera; identifying a set of image vectors associated with at least one predefined category of interest from the image vectors by a trained AI model; assigning at least one dimension to each of the set of image vectors; determining by the trained AI model, for a subset of image vectors within the set of image vectors, the availability of at least one relevant label from a plurality of pre-created labels; receiving a user input for assigning a new label to the subset of image vectors, in response to determining non-availability of a relevant label from the plurality of pre-created labels; performing incremental learning based on the new label received from the user by the trained AI model.