Image Segmentation by Using Linear Spectral Clustering
| Autor: | Reddy Ns |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Pixel
business.industry Computer science Segmentation-based object categorization Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Pattern recognition Image segmentation computer.software_genre Spectral clustering Computer Science::Computer Vision and Pattern Recognition Metric (mathematics) Artificial intelligence Data mining business Cluster analysis computer |
| Zdroj: | Journal of Telecommunications System & Management. |
| ISSN: | 2167-0919 |
| Popis: | In this paper a super pixel segmentation algorithm called Linear Spectral Clustering (LSC), which produces compact and uniform super pixels with low computational costs. Basically, a normalized cuts formulation of the super pixel segmentation is adopted based on a similarity metric that measures the colour similarity and space proximity between image pixels. However, instead of using the traditional Eigen-based algorithm, we approximate the similarity metric using a kernel function leading to an explicitly mapping of pixel values and coordinates into a high dimensional feature space. We revisit the conclusion that by appropriately weighting each point in this feature space, the objective functions of weighted K-means and normalized cuts share the same optimum point. As such, it is possible to optimize the cost function of normalized cuts by iteratively applying simple K-means clustering in the proposed feature space. LSC is of linear computational complexity and high memory efficiency and is able to preserve global properties of images. Experimental results show that LSC performs equally well or better than state of the art super pixel segmentation algorithms in terms of several commonly used evaluation metrics in image segmentation. |
| Databáze: | OpenAIRE |
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