IEEE Transactions on Pattern Analysis and Machine Intelligence· 2012Q1
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
- 9,138citations
- Q1SCImago
- 2012year
Short summary
A new superpixel algorithm, SLIC, adheres to image boundaries as well as or better than existing methods, while being faster, more memory-efficient, and improving segmentation performance.
AI-generated from the title and abstract; the full text is not read.
Key points
- SLIC adapts k-means clustering for efficient superpixel generation.
- SLIC adheres to image boundaries as well as or better than previous methods.
- SLIC is faster and more memory-efficient than existing superpixel algorithms.
- SLIC improves segmentation performance.
- SLIC is straightforward to extend to supervoxel generation.
AI-generated from the title and abstract; the full text is not read.
Abstract
Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
The authors' abstract, as published at the source. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science