Annotation Densification for Semantic Segmentation Boundary Accuracy
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Solution Overview
Problem
Semantic segmentation methods face challenges in achieving high-quality pixel-wise annotations due to the time-consuming and expensive process of generating detailed labels, often resorting to coarse labels that compromise boundary accuracy, limiting the effectiveness of training algorithms.
Innovation Solution
A method of annotation densification that combines image information, coarse annotation data, and predictions from existing semantic segmentation models through image matting and ConvNet processing to generate fine semantic segmentation annotations, enhancing the quality and quantity of training data without additional cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If coarse semantic labels are used for annotation, then annotation speed and cost are improved, but boundary accuracy and annotation quality deteriorate
Solution Approach 1:
The annotation process is segmented into multiple stages: initial coarse annotation followed by automated refinement using image matting algorithms and ConvNet models. This segmentation allows rapid initial labeling while systematically improving boundary accuracy through subsequent processing steps.
Solution Approach 2:
Coarse annotations are generated first as preliminary labels that provide initial semantic information. These preliminary annotations serve as input for automated refinement processes, enabling rapid initial coverage that is later enhanced without manual re-annotation.
2Measurement precision
If pixel-wise semantic labels are generated manually, then annotation quality and boundary accuracy are improved, but time consumption and cost increase
Solution Approach 1:
Automated algorithms serve as intermediaries between coarse annotations and final pixel-wise labels. Image matting algorithms and ConvNet models act as mediators that transform rough polygon annotations into detailed pixel-level semantic labels without requiring manual pixel-by-pixel labeling.
Solution Approach 2:
Manual mechanical labeling processes are replaced with automated computational systems. Image matting algorithms and deep learning models substitute human operators, automatically generating high-quality pixel-wise annotations from coarse inputs without human intervention in the refinement stage.
3Ease of manufacture
If coarse annotations with unlabeled boundaries are used, then annotation cost is reduced, but training effectiveness for semantic segmentation deteriorates
Solution Approach 1:
The annotation data undergoes parameter transformation from coarse polygon representations to detailed pixel-wise labels through automated processing. This parameter change enriches the training data with precise boundary information while maintaining the cost efficiency of initial coarse annotation.
Solution Approach 2:
The final annotation combines elements from multiple sources: initial coarse annotation data, image matting results, and ConvNet predictions. This composite approach integrates the cost efficiency of coarse labeling with the training effectiveness of detailed pixel-wise annotations.
Data Source
AI summary
Methods and systems of annotation densification for semantic segmentation are disclosed herein. In one example embodiment, such a method includes obtaining image information, obtaining coarse annotation information, performing an image matting operation based upon the image information and based at least indirectly upon the coarse annotation information, and applying an already-trained Convolutional Neural Network (ConvNet) semantic segmentation model in relation to the image information. The method also includes performing a merging operation with respect to both first intermediate information generated at least indirectly by the performing of the image matting operation and second intermediate information generated at least indirectly by the applying of the ConvNet model, where the performing of the merging operation results in fine semantic segmentation annotation information, whereby an additional semantic segmentation model can be trained using that annotation information and the trained additional semantic segmentation model can be applied to generate semantic segmentation output information.