A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut
| Autor: | Ying Li, Weizhong Zhang, Shuliang Wang, Zhenkuan Pan, Caoyuan Li |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Normalization (statistics)
Article Subject Computer science General Mathematics Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing 02 engineering and technology 01 natural sciences 010104 statistics & probability 0202 electrical engineering electronic engineering information engineering 0101 mathematics Cluster analysis Time complexity Pixel business.industry lcsh:Mathematics General Engineering Pattern recognition Image segmentation lcsh:QA1-939 lcsh:TA1-2040 Computer Science::Computer Vision and Pattern Recognition Pattern recognition (psychology) Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business |
| Zdroj: | Mathematical Problems in Engineering, Vol 2018 (2018) |
| ISSN: | 1563-5147 1024-123X |
| DOI: | 10.1155/2018/8508294 |
| Popis: | Color image segmentation is fundamental in image processing and computer vision. A novel approach, GDF-Ncut, is proposed to segment color images by integrating generalized data field (GDF) and improved normalized cuts (Ncut). To start with, the hierarchy-grid structure is constructed in the color feature space of an image in an attempt to reduce the time complexity but preserve the quality of image segmentation. Then a fast hierarchy-grid clustering is performed under GDF potential estimation and therefore image pixels are merged into disjoint oversegmented but meaningful initial regions. Finally, these regions are presented as a weighted undirected graph, upon which Ncut algorithm merges homogenous initial regions to achieve final image segmentation. The use of the fast clustering improves the effectiveness of Ncut because regions-based graph is constructed instead of pixel-based graph. Meanwhile, during the processes of Ncut matrix computation, oversegmented regions are grouped into homogeneous parts for greatly ameliorating the intermediate problems from GDF and accordingly decreasing the sensitivity to noise. Experimental results on a variety of color images demonstrate that the proposed method significantly reduces the time complexity while partitioning image into meaningful and physically connected regions. The method is potentially beneficial to serve object extraction and pattern recognition. |
| Databáze: | OpenAIRE |
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