Hybrid Classifier and Graph Cut for Ground Truth Generation
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Solution Overview
Problem
Creating a large number of ground truth images is labor-intensive and requires significant effort, especially when training data is limited, leading to inaccuracies in extracting target regions, particularly near boundaries.
Innovation Solution
An image processing apparatus that combines a trained classifier with a graph cut segmentation method, using region information to accurately extract target regions and generate ground truth images by setting foreground and background seeds, thereby improving extraction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a trained classifier is used to extract target regions, then extraction speed is improved, but extraction accuracy deteriorates near boundaries
Solution Approach 1:
The patent combines a trained classifier (first extraction unit) with a graph cut segmentation method (second extraction unit) into a hybrid system. The classifier provides initial target region extraction for speed, while the graph cut method refines the boundaries for accuracy. This merging allows the system to leverage both the efficiency of machine learning and the precision of traditional segmentation algorithms, resolving the contradiction between extraction speed and boundary accuracy.
Solution Approach 2:
The extraction process is divided into two sequential stages: first, the trained classifier performs rapid initial extraction to identify potential target regions; second, the graph cut segmentation method is applied specifically to refine the boundaries of these regions. This segmentation of the extraction process allows different methods to be applied at different stages, optimizing both speed and accuracy without requiring one method to handle the entire task alone.
2Measurement precision
If manual creation of ground truth images is performed, then extraction accuracy is improved, but productivity deteriorates
Solution Approach 1:
The system uses the hybrid extraction method (combining classifier and graph cut) to automatically generate ground truth images without requiring manual annotation. The apparatus extracts target regions using both the trained classifier and graph cut segmentation, then uses these automated extraction results as ground truth images for training the classifier. This self-service approach eliminates the need for manual ground truth creation while maintaining high accuracy, thereby resolving the contradiction between accuracy and productivity.
Solution Approach 2:
The system performs preliminary automated extraction using the hybrid method before manual verification is needed. By pre-processing images with both the classifier and graph cut segmentation, the system creates initial ground truth images that are already highly accurate, reducing or eliminating the need for subsequent manual creation efforts and significantly improving productivity.
3Device complexity
If limited training data is used, then device complexity is reduced, but extraction accuracy deteriorates
Solution Approach 1:
The patent merges a trained classifier with a graph cut segmentation method that does not require training data. This combination allows the system to achieve high extraction accuracy even with limited training data, because the graph cut method provides a traditional algorithmic approach that complements the machine learning classifier. The hybrid system leverages the strengths of both approaches, resolving the contradiction between simplicity (limited training data) and accuracy.
Data Source
AI summary
An image processing apparatus includes a first extraction unit configured to extract a first target region from an image using a trained classifier, a setting unit configured to set region information to be used in a graph cut segmentation method based on a first extraction result including the first target region, a second extraction unit configured to extract a second target region using the graph cut segmentation method based on the set region information, and a generation unit configured to generate a ground truth image corresponding to the image based on a second extraction result including the second target region.