Superpixel Merging Using Penalized Average Linkage Cuts
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Solution Overview
Problem
Current image editing tools, including those using superpixels, face challenges in efficiency and accuracy due to coarse selection methods and the trade-off between speed and quality in superpixel generation, with existing algorithms being either too slow or producing low-quality results.
Innovation Solution
A method for merging superpixels using Penalized Average Linkage Cuts (PALC) to select and combine superpixels, allowing for a faster generation of high-quality superpixels while reducing the number of superpixels, thereby improving editing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If watershed segmentation algorithm is used to generate superpixels, then the generation speed is fast, but the superpixel quality is very low when the number of superpixels is small
Solution Approach 1:
The patent applies preliminary action by first generating an initial set of superpixels using the fast watershed segmentation algorithm, then performing a subsequent merging step to improve quality. This two-stage approach allows the system to benefit from the speed of watershed segmentation while achieving higher quality through the refinement merging process that combines adjacent superpixels based on boundary strength and similarity metrics.
2Manufacturing precision
If high quality superpixel algorithms (SEEDS, linear spectral clustering, entropy rate) are used, then the superpixel quality is high, but the generation speed is one to three times slower than watershed segmentation
Solution Approach 1:
The patent combines the advantages of different approaches by merging adjacent superpixels based on calculated similarity values and boundary strength. This merging operation refines the initial superpixel segmentation achieved by fast algorithms, producing quality comparable to slower algorithms while maintaining the speed advantage of the initial fast generation phase.
3Manufacturing precision
If the number of superpixels is increased to enhance quality, then the superpixel quality is enhanced, but the processing time increases
Solution Approach 1:
The patent changes the parameter of superpixel count dynamically through the merging process. Instead of fixing the number of superpixels at generation, the system starts with a larger number from the fast watershed algorithm and merges them down to achieve the target quality and count, optimizing both speed and quality by avoiding unnecessary fine-grained segmentation.
4Productivity
If smart selection algorithms are used to extend the selected region, then the editing efficiency is increased, but the boundary preservation quality is poor and the user loses control
Solution Approach 1:
The patent applies local quality by treating different regions of the image differently during the merging process. The merging decision for each pair of superpixels is based on local boundary strength and local similarity metrics, allowing the algorithm to preserve important boundaries while merging suitable regions. This local decision-making maintains boundary preservation quality while achieving efficient segmentation.
Data Source
AI summary
Techniques are described for merging super pixels of an image. The image may include two superpixel, for which a similarity value is calculated. The similarity value is determined based on the link and cut values of the superpixels, the similarity value representing pixel-based similarity of the superpixels. The link value is determined based on the similarity between color values of the pixels in the superpixels, while the cut value is determined based on the edge pixels of the superpixels. Based on the calculated similarity value, the system determines whether to merge the superpixels and if so, merges the superpixels thereby generating another superpixel.