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
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1.
(江西理工大學(xué)信息工程學(xué)院 贛州 341000) ②(浙江大學(xué)計(jì)算機(jī)科學(xué)技術(shù)學(xué)院 杭州 310027)
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
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1.
(School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China)
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2.
(College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China)
Funds:
The National Natural Science Foundation of China (61105042, 61462035), The Young Scientist Training Project of Jiangxi Province (20153BCB23010)
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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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關(guān)鍵詞:
- 顯著性區(qū)域檢測(cè) /
- 多尺度融合 /
- KL散度 /
- 閉環(huán)連通圖
Abstract: A new salient region detection algorithm is proposed via KL divergence between color probability distributions of super-pixels and merging multi-scale saliency maps. Firstly, multi-scale super-pixel segmentations of an input image are computed. In each segmentation scale, an undirected close-loop connected graph is constructed, in which nodes are the super-pixels and the adjacent regions are expanded reasonably relying on the total number of super-pixels. Then, all the color values in each super-pixel are clustered in terms of their discriminative power to get the statistical probability distribution of the cluster labels for each super-pixel. Next, the edges between all adjacent super-pixel pairs are weighted with the harmonic-mean of KL divergence of their probability distributions, and then the multi-scale saliency maps are calculated according to boundary connectivity and region contrast. The final saliency map is obtained by calculating and optimizing the mean map of all the saliency maps with different scales. Experimental results on some large benchmark datasets demonstrate that the proposed algorithm outperforms some state-of-the-art methods, and has higher precision and recall rates. The proposed algorithm can also produce smooth saliency maps. -
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