Gradient-Based Data Classification and ROI Segmentation
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Solution Overview
Problem
Existing data classification and object detection methods, such as neural networks and SVMs, require extensive testing across all data positions, leading to increased test duration and reduced detection accuracy.
Innovation Solution
A method and apparatus that select a hypothesis class among entire classes, modify input data based on a gradient vector to increase the value of the hypothesis class, and segment regions of interest (ROI) using algorithms like graph cut or CRF, allowing for efficient re-classification and re-segmentation based on preset conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general machine learning methods (neural networks, CNNs, SVMs) are used to classify data, then detection coverage is comprehensive, but test duration increases and detection accuracy reduces
Solution Approach 1:
The patent segments the data classification process into multiple stages: initial classification using general machine learning methods, followed by gradient-based refinement in specific regions. This segmentation allows the system to avoid exhaustive testing across all data positions while maintaining high detection accuracy in critical areas through iterative gradient descent optimization.
Solution Approach 2:
The patent applies local quality by using gradient information to identify and focus computational resources on specific regions where classification uncertainty exists. Instead of uniformly processing all data, the system dynamically adjusts classification effort based on local gradient magnitudes, allocating more resources to regions with higher uncertainty and fewer resources to confidently classified regions.
2Reliability
If exhaustive testing is performed on every available position of data, then detection coverage is maximized, but test duration increases
Solution Approach 1:
The patent performs preliminary classification using general machine learning methods before applying gradient-based refinement. This preliminary action identifies regions that require further analysis, allowing the system to avoid exhaustive testing in areas that are already confidently classified while focusing computational resources on ambiguous regions.
Solution Approach 2:
The patent applies partial action by performing gradient-based refinement only in regions where it is necessary to improve detection accuracy, rather than applying exhaustive testing uniformly across all data. The system dynamically determines the extent of refinement needed based on gradient magnitudes and classification confidence levels.
3Measurement precision
If multiple re-classifications and re-segmentations are performed to improve accuracy, then detection accuracy improves, but computational complexity and time increase
Solution Approach 1:
The patent introduces dynamics by making the classification process adaptive and iterative. The system performs multiple rounds of gradient-based refinement, dynamically adjusting classification decisions based on gradient information from previous iterations. This dynamic approach allows the system to converge to high-accuracy solutions while avoiding unnecessary computational complexity through early termination when convergence criteria are met.
Solution Approach 2:
The patent implements feedback mechanisms where gradient information from classification results is used to guide subsequent refinement steps. The system continuously monitors classification confidence and gradient magnitudes, using this feedback to determine when further refinement is necessary and when to terminate the iterative process, thereby balancing accuracy improvement with computational efficiency.
Data Source
AI summary
A method and an apparatus are described to classify data. The method and apparatus includes selecting a hypothesis class among entire classes. The method and corresponding apparatus generate output data with regard to the entire classes by applying a classification algorithm to input data, and modify the input data to increase a value of the hypothesis class among the output data in response to a re-classification condition being met. The modified input data is set to be new input data.