Human-Assisted Machine Learning via Geometric Refinement
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Solution Overview
Problem
Machine learning techniques for digital image processing are complex and time-consuming for users unfamiliar with existing algorithms, requiring extensive training and intuitive visualization tools to diagnose and correct errors effectively.
Innovation Solution
A human-assisted machine learning system with a graphical user interface for managing files, visualizing detected features, correcting misclassifications, and configuring machine learning parameters, allowing users to create and refine image classifiers using geometric recursion and real-time visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used to convert images into geometric labels, then image processing capability is improved, but user accessibility and ease of operation deteriorate due to complexity and time consumption
Solution Approach 1:
The patent introduces an intermediary system that translates complex machine learning operations into simple geometric manipulation tasks. Users interact with intuitive geometric representations (polygons, polylines) rather than raw pixel data or complex algorithms, serving as a mediator between user intent and machine learning processing.
Solution Approach 2:
The patent replaces traditional mechanical/image processing approaches with geometric abstraction. Instead of manipulating pixels directly through complex algorithms, the system substitutes this with geometric shape manipulation and refinement, making the process more intuitive and accessible to non-experts.
2Measurement precision
If advanced machine learning algorithms are applied, then classification accuracy is improved, but system complexity and difficulty of diagnosis increase
Solution Approach 1:
The patent segments the complex machine learning process into distinct geometric manipulation stages: initial geometric label creation, refinement through vertex adjustment, and iterative improvement. This segmentation makes each step manageable and diagnosable while maintaining overall classification accuracy.
Solution Approach 2:
The patent uses visual feedback mechanisms where geometric elements change appearance (color, highlighting) to indicate classification confidence, error states, and refinement opportunities. This visual language provides intuitive diagnosis without requiring users to understand underlying algorithmic complexity.
3Manufacturing precision
If geometric refinement through vertex manipulation is implemented, then manufacturing precision of geometric labels is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary geometric label generation using automated algorithms before user refinement. This preliminary action provides a good initial approximation, reducing the amount of manual refinement needed and thus reducing overall time consumption while maintaining high precision.
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates geometric label quality and provides guidance on which vertices to refine first. This feedback-driven approach prioritizes refinement efforts on areas that most impact classification accuracy, reducing unnecessary time spent on already-accurate regions.
Data Source
AI summary
Systems and methods are provided for feature detection such that users can apply advanced machine learning and artificial intelligence (AI) without the need for a deep understanding of existing algorithms and techniques. Embodiments of the present disclosure provide systems and methods than enable easy access to a suite of machine learning algorithms and techniques, an intuitive interface for training an AI to recognize image features based on geometric “correct and refine” recursion, and real-time visualizations of training effectiveness.