Recursive Context Propagation for Image Semantic Labeling
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Solution Overview
Problem
Existing semantic labeling methods face challenges in accurately and efficiently labeling images due to the diversity of semantic concepts and the limitations of model-based and non-parametric methods, including slow processing times and limited scalability.
Innovation Solution
A method utilizing recursive context propagation, where local regions are classified based on extracted features, and information is recursively combined and decombined to form enhanced semantic features that capture both local and contextual information, enabling fast and scalable semantic labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-parametric methods use a large pre-labeled image database to improve labeling accuracy, then the accuracy improves, but the retrieval time increases significantly
Solution Approach 1:
The patent segments the image into superpixels first, then performs semantic labeling on these smaller units. This segmentation allows the method to process local regions independently and combine results, avoiding the need to process entire large images sequentially and enabling parallel processing of multiple regions.
Solution Approach 2:
The patent introduces a hierarchical dimension by organizing superpixels into a tree structure where leaf nodes represent superpixels and internal nodes represent groups of superpixels. This hierarchical organization enables multi-scale processing and allows information to be propagated efficiently through the tree structure, improving both accuracy and speed.
2Measurement precision
If model-based methods use complex parametric models and optimization problems to improve labeling accuracy, then the accuracy improves, but the processing speed decreases
Solution Approach 1:
The patent performs preliminary action by first segmenting the image into superpixels and extracting features before the actual semantic labeling process. This preliminary organization of data into a structured hierarchical format prepares the input for faster processing during the inference stage, avoiding the need for complex optimization during the main processing phase.
Solution Approach 2:
The patent replaces the mechanical optimization process with a neural network-based inference system. Instead of solving complex optimization problems iteratively, the trained neural network directly predicts semantic labels based on extracted features, significantly accelerating the processing speed while maintaining accuracy.
3Productivity
If per-pixel classification is performed without context propagation to improve processing speed, then the speed improves, but the labeling accuracy deteriorates
Solution Approach 1:
The patent implements feedback through recursive context propagation where semantic information is propagated from parent nodes to child nodes in the hierarchical tree structure. This feedback mechanism allows each superpixel to receive contextual information from its groupings, improving labeling accuracy without requiring complex iterative optimization, thus maintaining fast processing speed.
Solution Approach 2:
The patent adds the hierarchical dimension to the processing architecture, organizing superpixels into a tree structure that enables multi-scale context propagation. This hierarchical organization allows context to be propagated efficiently through the tree without requiring pairwise comparisons between all pixels, maintaining computational efficiency while improving accuracy through contextual information.
Data Source
AI summary
A method semantically labels an image acquired of a scene by first obtaining a local semantic feature for each local region in the image. The local semantic features are combined recursively to form intermediate segments until a semantic feature for the entire image is obtained. Then, the semantic feature for the entire image is decombined recursively into intermediate segments until an enhanced semantic feature for each local region is obtained. Then, each local region is labeled according to the enhanced semantic feature.