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Content-sensitive superpixel segmentation via self-organization-map neural network.
- Source :
-
Journal of Visual Communication & Image Representation . Aug2019, Vol. 63, pN.PAG-N.PAG. 1p. - Publication Year :
- 2019
-
Abstract
- • Present a novel metric for the content-sensitiveness of superpixel. • Proposed a content-sensitive sampling algorithm to generate content-sensitive superpixel. • Propose a novel superpixel segmentation algorithm based on SOM. Content-sensitive superpixel segmentation generates small superpixels in content-dense regions and large superpixels in content-sparse regions. It achieves higher segmentation accuracy than traditional superpixels. In this paper, we propose a content-sensitive superpixel segmentation algorithm based on Self-Organization-Map (SOM) neural network. First, we propose a novel metric to measure the content-sensitiveness of superpixels. Second, by using this metric, we develop a sampling algorithm to sample pixels from image according to their content-sensitiveness. Finally, a SOM neutral network is trained with the sampled pixels and used to segment the image into content-sensitive superpixels. The Berkeley Image Segmentation database and INRIA database are used to evaluate the proposed method. The experiment results show that the proposed approach outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PIXELS
*NEURAL computers
*IMAGE segmentation
*SELF-organizing maps
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 63
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 138254243
- Full Text :
- https://doi.org/10.1016/j.jvcir.2019.102572