Improved accuracy of superpixel segmentation by region merging method
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Superpixel as an important pre-processing technique has been successfully used in many vision applications. In this paper, we proposed a region merging method to improve superpixel segmentation accuracy with low computational cost. We first segmented the image into many accurate small regions, and then progressively agglomerated them until the desired region number was reached. The region merging weight was derived from a novel energy function, which encourages the superpixel with color consistency and similar size. Experimental results on the Berkeley BSDS500 data set showed that our region merging method can significantly improve the accuracy of superpixel segmentation. Moreover, the region merging method only need 50 ms to process a 481 × 321 image on a single Intel i3 CPU at 2.5 GHz.
Keywordsimage processing image segmentation superpixels region merging
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- 3.Pantofaru C, Schmid C, Hebert M. Object recognition by integrating multiple image segmentations. In: Proceedings of European Conference on Computer Vision. 2008, 5304: 481–494Google Scholar
- 4.Fulkerson B, Vedaldi A, Soatto S. Class segmentation and object localization with superpixel neighborhoods. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 670–677Google Scholar
- 7.Veksler O, Boykov Y, Mehrani P. Superpixels and supervoxels in an energy optimization framework. In: Proceedings of European Conference on Computer Vision. 2010, 6315: 211–224Google Scholar
- 16.Wikipedia. Diversity index, 2014, http://en.wikipedia.org/wiki/Diversity_indexGoogle Scholar