Geodesic Feature Image Labeling with Entangled Forests
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Solution Overview
Problem
Existing image labeling technologies face limitations in accuracy and efficiency due to the need for multiple processing stages, which increases complexity and time costs, and struggle to effectively utilize geodesic features for accurate image segmentation.
Innovation Solution
An automated classifier using geodesic features and entangled random decision forests with auto-context, employing parallel processing and look-up procedures to enhance image labeling accuracy and reduce operation time, particularly in medical and street scene image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate stages of processing are used to improve image labeling accuracy, then accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The patent combines multiple processing stages into a unified random decision forest framework that simultaneously performs classification and geodesic distance computation. The entangled random decision forest integrates feature extraction, classification, and geometric constraint application in a single cohesive system, eliminating the need for separate processing stages while maintaining high accuracy.
Solution Approach 2:
The random decision forest classifier serves multiple functions simultaneously: it performs image element classification, computes geodesic distances, and applies geometric constraints. This multi-functional approach replaces multiple specialized processing stages, reducing system complexity while preserving the accuracy benefits of each individual stage.
2Measurement precision
If multiple separate stages of processing are used to improve image labeling accuracy, then accuracy is improved, but processing time increases
Solution Approach 1:
The patent pre-computes geodesic distance maps and stores them in lookup tables during an offline training phase. During actual image labeling, the system directly retrieves pre-computed geodesic distances instead of computing them in real-time, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The system creates pre-computed copies of geodesic distance information in the form of lookup tables and training data. These copies are generated once during training and then reused repeatedly during inference, eliminating the need for repeated computation and大幅 reducing processing time.
3Manufacturing precision
If geodesic features are used to improve segmentation accuracy, then segmentation precision is improved, but computational complexity increases
Solution Approach 1:
The patent pre-computes geodesic distance maps and stores them in lookup tables during training. This preliminary computation transforms the complex geodesic feature extraction into a simple lookup operation during inference, maintaining segmentation precision while dramatically reducing computational complexity.
Solution Approach 2:
The patent introduces geodesic distance maps as intermediary structures that mediate between raw image data and final segmentation results. These maps pre-process geometric information in a way that simplifies subsequent classification operations, reducing computational complexity while preserving segmentation accuracy.
4Manufacturing precision
If entangled random decision forest with auto-context is used to improve labeling accuracy, then segmentation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent implements auto-context by nesting multiple levels of random decision forests where the output of one level becomes input to the next. This nested structure allows the model to iteratively refine predictions using contextual information from previous levels, improving segmentation accuracy while organizing complexity in a hierarchical manner that is more manageable than flat complex models.
Data Source
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AI summary
Image labeling is described, for example, to recognize body organs in a medical image, to label body parts in a depth image of a game player, to label objects in a video of a scene. In various embodiments an automated classifier uses geodesic features of an image, and optionally other types of features, to semantically segment an image. For example, the geodesic features relate to a distance between image elements, the distance taking into account information about image content between the image elements. In some examples the automated classifier is an entangled random decision forest in which data accumulated at earlier tree levels is used to make decisions at later tree levels. In some examples the automated classifier has auto-context by comprising two or more random decision forests. In various examples parallel processing and look up procedures are used.