一種新的圖像超像素分割方法
doi: 10.11999/JEIT190111
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湖南科技大學計算機科學與工程學院 湘潭 411100
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中南大學計算機學院 長沙 410083
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3.
中南大學自動化學院 長沙 410083
A New Method for Image Superpixel Segmentation
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School of Computer Science and Engineering, Hunan University of Science andTechnology, Xiangtan 411100, China
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School of Computer Science and Engineering, Central South University, Changsha 410083, China
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School of Automation, Central South University, Changsha 410083, China
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摘要:
針對現(xiàn)有超像素分割方法無法自動確定合適的超像素數(shù)目，以及難以有效貼合圖像目標邊界等問題，該文提出一種新的利用局部信息進行多層級簡單線性迭代聚類的圖像超像素分割方法。首先，運用基于局部信息的簡單線性迭代聚類(LI-SLIC)對原始圖像進行超像素初分割，然后，根據(jù)超像素的色彩標準差對其進行自適應(yīng)多層級迭代分割，直至每個超像素塊的色彩標準差小于預(yù)設(shè)閾值，最后，利用相鄰超像素間的色彩差異對過分割的超像素進行合并。為驗證方法的有效性，該文采用Berkeley, Pascal VOC和3Dircadb公共數(shù)據(jù)庫作為實驗數(shù)據(jù)集，并與其他多種超像素分割方法進行了比較。實驗結(jié)果表明，該文提出的超像素分割方法能更精確貼合圖像目標邊界，有效抑制圖像過分割和欠分割。
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關(guān)鍵詞:
- 圖像處理 /
- 超像素 /
- 局部信息簡單線性迭代聚類 /
- 多層級迭代分割 /
- 超像素合并
Abstract:Considering the problem that the existing superpixel methods are usually unable to set an appropriate number of generated superpixels automatically and unable to adhere to image boundaries effectively, a new superpixel method is proposed in this paper, which utilizes local information to perform multi-level simple linear iterative clustering to generate superpixels. First, original image is initially segmented by Simple Liner Iterative Clustering based on Local Information (LI-SLIC). Then, each superpixel is segmented iteratively until its color standard deviation is lower than a predefined threshold. Finally, the over-segmented superpixels are merged based on the color differences between adjacent superpixels. Experiments on Berkeley, Pascal VOC and 3Dircadb databases, as well as comparison with other methods indicate that the proposed method can adhere to image boundaries more accurately, and can prevent over- and under- segmentations more effectively.
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