Classifier for Image Segmentation Using Photographic and 3D Data
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Solution Overview
Problem
Existing image labeling methods require a large number of manually labeled image segments for accurate classification, which is time-consuming and labor-intensive, and they often struggle to differentiate between similar image segments using only photographic data.
Innovation Solution
A system and method that utilize two sub-classifiers, one trained on photographic data and the other on 3D point data, iteratively refine their labeling by updating the pre-labeled segments, allowing for accurate image segmentation with a reduced number of initial manual labels by leveraging both data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sub-classifiers are used to improve segmentation accuracy, then classification precision improves, but system complexity increases
Solution Approach 1:
The patent divides the classification task into multiple sub-classifiers, each specializing in specific image segments or features. This segmentation of the classification function allows each sub-classifier to achieve higher precision on its specialized domain while the overall system maintains manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces a hierarchical dimension to the classification system by organizing sub-classifiers in multiple levels. This dimensional organization allows the system to achieve high accuracy through layered processing without proportionally increasing overall system complexity, as each layer builds upon previous classifications.
2Measurement precision
If more manual labels are used to train classifiers, then classification accuracy improves, but time consumption increases
Solution Approach 1:
The patent performs preliminary classification using a small set of manual labels to create initial training data. This preliminary action establishes a foundation that can be iteratively improved, reducing the total time required for comprehensive manual labeling while maintaining high accuracy through subsequent automated refinement cycles.
Solution Approach 2:
The patent implements iterative feedback loops where classification results are continuously refined using both manual and automated labeling. This feedback mechanism allows the system to progressively improve accuracy without requiring complete manual labeling upfront, as each iteration builds upon previous results and incorporates new learned patterns.
Data Source
AI summary
Systems, methods, and machine-readable media for generating a classifier configured to label segments of an image, are discussed. According to one aspect, the system may include a training module, a labeling module, and an update module. The training module may be configured to train a first sub-classifier based on photographic data for a set of pre-labeled image segments and a second sub-classifier based on 3-dimensional point data for the set of pre-labeled image segments. The labeling module may be configured to generate a labeling solution comprising a plurality of associations between an image segment from the set of unlabeled image segments and a label. The update module may be configured to update the set of pre-labeled image segments based on the labeling solution. The training module may also be configured to train the first sub-classifier and the second sub-classifier based on the updated set of pre-labeled image segments.