Image Segmentation Using Linked Mean-Shift Vectors and Global/Local Attributes
| Autor: | Young Hwan Kim, Suk-Ju Kang, Hanjoo Cho |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Segmentation-based object categorization Rand index Scale-space segmentation 020206 networking & telecommunications Pattern recognition 02 engineering and technology Image segmentation Minimum spanning tree-based segmentation Image texture 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Segmentation Artificial intelligence Electrical and Electronic Engineering Range segmentation business Mathematics |
| Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 27:2132-2140 |
| ISSN: | 1558-2205 1051-8215 |
| Popis: | This paper proposes novel noniterative mean-shift-based image segmentation that uses global and local attributes. The existing mean-shift-based methods use a fixed range bandwidth, and hence their accuracy is dependent on the range spectrum of an image. To resolve this dependency, this paper proposes to modify the range kernel in the mean-shift process to be anisotropic. The modification is conducted using a global attribute defined as the range covariance matrix of the image. Further, to alleviate oversegmentation, the proposed method merges the segments having similar local attributes more aggressively than other segments. The local attribute for each segment is defined as the sum of the variances of the chromatic components. Finally, to expedite the processing, the proposed method uses a region adjacency graph (RAG) for the merging process, thus differing from the existing linked mean-shift-based methods. In the experiments on the Berkeley segmentation data set, the use of the global and local attributes improved segmentation accuracy; the proposed method outperformed the state-of-the-art linked mean-shift-based method by showing an improvement of 2.15%, 3.16%, 3.32%, and 1.90% in probability rand index, segmentation covering, variation of information, and F-measure, respectively. Further, compared with the benchmark method, which uses the dilating and merging scheme, the proposed method improved the speed of the merging process 42 times by applying the RAG. |
| Databáze: | OpenAIRE |
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