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基于KL散度及多尺度融合的顯著性區(qū)域檢測(cè)算法

羅會(huì)蘭 萬(wàn)成濤 孔繁勝

羅會(huì)蘭, 萬(wàn)成濤, 孔繁勝. 基于KL散度及多尺度融合的顯著性區(qū)域檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1594-1601. doi: 10.11999/JEIT151145
引用本文: 羅會(huì)蘭, 萬(wàn)成濤, 孔繁勝. 基于KL散度及多尺度融合的顯著性區(qū)域檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1594-1601. doi: 10.11999/JEIT151145
LUO Huilan, WAN Chengtao, KONG Fansheng. Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1594-1601. doi: 10.11999/JEIT151145
Citation: LUO Huilan, WAN Chengtao, KONG Fansheng. Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1594-1601. doi: 10.11999/JEIT151145

基于KL散度及多尺度融合的顯著性區(qū)域檢測(cè)算法

doi: 10.11999/JEIT151145
基金項(xiàng)目: 

國(guó)家自然科學(xué)基金(61105042, 61462035),江西省青年科學(xué)家培養(yǎng)項(xiàng)目(20153BCB23010)

Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging

Funds: 

The National Natural Science Foundation of China (61105042, 61462035), The Young Scientist Training Project of Jiangxi Province (20153BCB23010)

  • 摘要: 基于對(duì)超像素顏色概率分布間KL散度的計(jì)算,以及對(duì)多尺度顯著圖的融合處理,該文提出一種新的顯著性區(qū)域檢測(cè)算法。首先,采用超像素算法多尺度分割圖像,在各尺度下用分割產(chǎn)生的超像素為節(jié)點(diǎn),并依據(jù)超像素分割數(shù)量對(duì)各超像素進(jìn)行適當(dāng)鄰接連通擴(kuò)展,構(gòu)建無(wú)向擴(kuò)展閉環(huán)連通圖。 其次,依據(jù)顏色判別力聚類(lèi)量化各超像素內(nèi)顏色,統(tǒng)計(jì)顏色聚類(lèi)標(biāo)簽的概率分布,用概率分布間KL散度的調(diào)和平均值為擴(kuò)展閉環(huán)連通圖的邊加權(quán),再依據(jù)區(qū)域?qū)Ρ榷炔⒔Y(jié)合邊界連通性,獲取各尺度下的顯著圖。 最后,平均融合各尺度下顯著圖,并進(jìn)行優(yōu)化處理,得到最終的顯著圖。 在一些大型參考數(shù)據(jù)集上進(jìn)行大量實(shí)驗(yàn)表明,所提算法優(yōu)于當(dāng)前一些先進(jìn)算法,具有較高精確度和召回率,并且可以產(chǎn)生平滑顯著圖。
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出版歷程
  • 收稿日期:  2015-10-13
  • 修回日期:  2016-03-15
  • 刊出日期:  2016-07-19

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