The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
SEEDS: Superpixels Extracted Via Energy-Driven Sampling
Abstract
Superpixel algorithms aim to over-segment the image by grouping pixels that belong to the same object. Many state-of-the-art superpixel algorithms rely on minimizing objective functions to enforce color homogeneity. The optimization is accomplished by sophisticated methods that progressively build the superpixels, typically by adding cuts or growing superpixels. As a result, they are computationally too expensive for real-time applications. We introduce a new approach based on a simple hill-climbing optimization. Starting from an initial superpixel partitioning, it continuously refines the superpixels by modifying the boundaries. We define a robust and fast to evaluate energy function, based on enforcing color similarity between the boundaries and the superpixel color histogram. In a series of experiments, we show that we achieve an excellent compromise between accuracy and efficiency. We are able to achieve a performance comparable to the state-of-the-art, but in real-time on a single Intel i7 CPU at 2.8 GHz.
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Cites
Normalized cuts and image segmentation
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Mean shift: a robust approach toward feature space analysis
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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
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Efficient Graph-Based Image Segmentation
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000022288.19776.77 · Cited by 6,176
Watersheds in digital spaces: an efficient algorithm based on immersion simulations
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Contour Detection and Hierarchical Image Segmentation
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TurboPixels: Fast Superpixels Using Geometric Flows
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Cited by
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Hosang, J., Benenson, R., Dollar, P., & Schiele, B. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2465908 · Cited by 727
Cheng, Y., Bai, H., Li, Z., Zhang, Y., Chen, L., & Chen, K. (2022) · Fire Technology · doi:10.1007/s10694-022-01214-5 · Cited by 6
Metadata
- Authors
- Van den Bergh, M., Boix, X., Roig, G., & Van Gool, L.
- Venue
- International Journal of Computer Vision
- Published
- 2014-07-19
- Volume
- 111
- Issue
- 3
- Pages
- 298-314
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2804865177
- Cited by
- 253
- References
- 26
- FWCI
- 11.57
- Type
- journal article
- Language
- en
- Open access
- closed
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