區(qū)域信息驅(qū)動(dòng)的多目標(biāo)進(jìn)化半監(jiān)督模糊聚類圖像分割算法

趙鳳; 張咪咪; 劉漢強(qiáng)

doi:10.12000/JRIT180605

區(qū)域信息驅(qū)動(dòng)的多目標(biāo)進(jìn)化半監(jiān)督模糊聚類圖像分割算法

doi: 10.12000/JRIT180605

趙鳳^{1, 2, ,},
張咪咪^{1, 2},
劉漢強(qiáng)³

1.
西安郵電大學(xué)通信與信息工程學(xué)院 ??西安 ??710121
2.
西安郵電大學(xué)電子信息現(xiàn)場(chǎng)勘驗(yàn)應(yīng)用技術(shù)公安部重點(diǎn)實(shí)驗(yàn)室 ??西安 ??710121
3.
陜西師范大學(xué)計(jì)算機(jī)科學(xué)學(xué)院 ??西安 ??710119

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61571361, 61102095, 61671377)，西安郵電大學(xué)西郵新星團(tuán)隊(duì)(xyt2016-01)

詳細(xì)信息

作者簡(jiǎn)介:
趙鳳：女，1980 年生，教授，研究方向?yàn)橛?jì)算智能與圖像處理

張咪咪：女，1992年生，碩士生，研究方向?yàn)閳D像處理

劉漢強(qiáng)：男，1981年生，副教授，研究方向?yàn)槟Ｊ阶R(shí)別與圖像處理

通訊作者:
趙鳳　fzhao.xupt@gmail.com

中圖分類號(hào): TP391
計(jì)量
- 文章訪問數(shù): 1878
- HTML全文瀏覽量: 840
- PDF下載量: 93
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-06-20
- 修回日期: 2018-12-14
- 網(wǎng)絡(luò)出版日期: 2019-01-18
- 刊出日期: 2019-05-01

Multi-objective Evolutionary Semi-supervised Fuzzy Clustering Image Segmentation Motivated by Region Information

Feng ZHAO^{1, 2
, ,},
Mimi ZHANG^{1, 2},
Hanqiang LIU³

1.
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Key Laboratory of Electronic Information Application Technology for Scene Investigation of Ministry of Public Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
3.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China

Funds: The National Natural Science Foundation of China (61571361, 61102095, 61671377), New Star Team of Xi’an University of Posts & Telecommunications (xyt2016-01)

摘要

摘要:
現(xiàn)有的多目標(biāo)進(jìn)化聚類算法應(yīng)用于圖像分割時(shí)，往往是在圖像像素層面上進(jìn)行聚類，運(yùn)行時(shí)間過長(zhǎng)，而且忽略了圖像區(qū)域信息使得圖像分割效果不太理想。為了提高多目標(biāo)進(jìn)化聚類算法的分割效果和時(shí)間效率，該文將圖像區(qū)域信息與部分監(jiān)督信息引入多目標(biāo)進(jìn)化聚類，提出圖像區(qū)域信息驅(qū)動(dòng)的多目標(biāo)進(jìn)化半監(jiān)督模糊聚類圖像分割算法。該算法首先利用超像素策略獲得圖像的區(qū)域信息，然后結(jié)合部分監(jiān)督信息，設(shè)計(jì)融合區(qū)域信息和監(jiān)督信息的適應(yīng)度函數(shù)，接著通過多目標(biāo)進(jìn)化策略對(duì)多個(gè)適應(yīng)度函數(shù)進(jìn)行優(yōu)化得到最優(yōu)解集。最后構(gòu)造融合區(qū)域信息與監(jiān)督信息的最優(yōu)解評(píng)價(jià)指標(biāo)，實(shí)現(xiàn)從最優(yōu)解集中選取一個(gè)最優(yōu)解。實(shí)驗(yàn)結(jié)果表明：與已有多目標(biāo)進(jìn)化聚類算法相比，該算法不但分割效果有所提升，而且運(yùn)行效率得以提高。
- 圖像分割 /
- 多目標(biāo)進(jìn)化 /
- 模糊聚類 /
- 半監(jiān)督聚類 /
- 區(qū)域信息
Abstract:
When multi-objective evolutionary clustering algorithms are applied to image segmentation, the image pixels are always utilized to be clustered. It results in a long running time. In addition, due to not considering the image region information, the image segmentation effect is not ideal. In order to improve the segmentation effect and time efficiency of the multi-objective evolutionary clustering algorithm, the image region information and some supervised information are introduced into multi-objective evolutionary clustering. Then a multi-objective evolutionary semi-supervised fuzzy clustering image segmentation algorithm driven by image region information is presented. First, the region information of the image is obtained through the super-pixel strategy. Second, two novel fitness functions are designed by introducing the supervised information and region information. Third, the multi-objective evolutionary strategy is used to optimize these two fitness functions to obtain an optimal solution set. Finally, an optimal solution evaluation index with region information and supervision information is constructed and utilized to select an optimal solution from the optimal solution set. Experimental results show the proposed algorithm outperforms comparison methods in segmentation performance and running efficiency.
- Image segmentation /
- Multi-objective evolutionary optimization /
- Fuzzy clustering /
- Semi-supervised clustering /
- Region information

HTML全文

圖 1 準(zhǔn)確率隨$\alpha $變化折線圖

下載: 全尺寸圖片幻燈片

圖 2 #135069分割結(jié)果圖

下載: 全尺寸圖片幻燈片

圖 3 #124084分割結(jié)果圖

下載: 全尺寸圖片幻燈片

表 1 各算法聚類準(zhǔn)確率對(duì)比

圖像	FCM	SSFCM	SSFC-SC	MOVGA	本文算法
#3096	0.9859	0.9860	0.9865	0.5388	0.9931
#135069	0.7368	0.9926	0.9924	0.3301	0.9925
#118035	0.9342	0.9342	0.9337	0.9367	0.9523
#124084	0.7415	0.7418	0.8465	0.8678	0.9457
#86016	0.8394	0.8395	0.8568	0.6190	0.9811
#161062	0.8846	0.8847	0.8988	0.5711	0.9830
#260058	0.7893	0.7898	0.8301	0.3730	0.9904
#8068	0.9517	0.9518	0.9518	0.7112	0.9858
#113044	0.8381	0.8384	0.8395	0.2664	0.9330
#12003	0.7737	0.7735	0.8079	0.4421	0.8919
#296059	0.7397	0.7396	0.7400	0.6364	0.9284
#238011	0.8093	0.9565	0.9565	0.9566	0.9605
#101027	0.8839	0.8840	0.8850	0.5689	0.9024
#28075	0.4479	0.4456	0.5666	0.5873	0.9374
#24063	0.9675	0.9675	0.9696	0.9601	0.9737
#253036	0.6193	0.6195	0.6921	0.6443	0.9448
#42044	0.7524	0.7526	0.7572	0.7055	0.8595
#299091	0.6962	0.6964	0.7220	0.3360	0.9564
#113016	0.8164	0.8142	0.8843	0.7203	0.9426
#147091	0.9316	0.9317	0.9314	0.7781	0.9041

下載: 導(dǎo)出CSV

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