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SLIC superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta1, Appu Shaji, Kevin Smith
1School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. radhakrishna.achanta@epfl.ch
Summary
We introduce Simple Linear Iterative Clustering (SLIC), a new superpixel algorithm that adheres to image boundaries effectively. SLIC offers improved speed and memory efficiency for computer vision tasks.
Area of Science:
- Computer Vision
- Image Processing
- Machine Learning
Background:
- Superpixels are crucial for modern computer vision, yet algorithm performance varies.
- Evaluating existing superpixel algorithms is essential for advancing image segmentation.
Purpose of the Study:
- To empirically compare state-of-the-art superpixel algorithms.
- To introduce and evaluate a novel superpixel algorithm, SLIC.
Main Methods:
- Comparative analysis of five superpixel algorithms based on boundary adherence, speed, and memory.
- Introduction of Simple Linear Iterative Clustering (SLIC), a k-means based approach.
Main Results:
- SLIC demonstrates comparable or superior boundary adherence to existing methods.
- SLIC achieves higher speed and memory efficiency.
- Improved segmentation performance is observed with SLIC.
Conclusions:
- SLIC offers a simple, efficient, and effective superpixel generation method.
- The algorithm shows potential for extension to supervoxel generation for 3D data.
