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
-
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)
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
-
1.
(School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China)
-
2.
(College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China)
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。
-
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. -
ITTI L, KOCH C, and NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259. YANG J and YANG M H. Top-down visual saliency via joint CRF and dictionary learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2012: 2296-2303. TONG N, LU H, RUAN X, et al. Salient object detection via bootstrap learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015: 1884-1892. ZHAO R, OUYANG W, LI H, et al. Saliency detection by multi-context deep learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015: 1265-1274. YAN Q, XU L, SHI J, et al. Hierarchical saliency detection [C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013: 1155-1162. ZHU W, LIANG S, WEI Y, et al. Saliency optimization from robust background detection[C]. IEEE International Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 2814-2821. YANG C, ZHANG L, LU H, et al. Saliency detection via graph-based manifold ranking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3166-3173. TONG N, LU H, ZHANG Y, et al. Salient object detection via global and local cues[J]. Pattern Recognition, 2015, 48(10): 3258-3267. KIM J, HAN D, TAI Y W, et al. Salient region detection via high-dimensional color transform[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 883-890. ACHANTA R, ESTRADA F, WILS P, et al. Salient region detection and segmentation[C]. International Conference on Computer Vision Systems, Heraklion, 2008: 66-75. CHENG M M, ZHANG G X, MITRA N J, et al. Global contrast based salient region detection[C]. IEEE International Conference on Computer Vision and Pattern Recognition, Colorado Springs, 2011: 409-416. PERAZZI F, KRAHENBUHL P, PRITCH Y, et al. Saliency filters: Contrast based filtering for salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 733-740. HOU X and ZHANG L. Saliency detection: A spectral residual approach[C]. IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, 2007: 1-8. ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency- tuned salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009: 1597-1604. 呂建勇, 唐振民. 一種基于圖的流形排序的顯著性目標(biāo)檢測(cè)改進(jìn)方法[J]. 電子與信息學(xué)報(bào), 2015, 37(11): 2555-2563. doi: 10.11999/JEIT150619. Jianyong and TANG Zhenmin. An improved graph-based manifold ranking for salient object detection[J]. Journal of Electronics Information Technology, 2015, 37(11): 2555-2563. doi: 10.11999/JEIT150619. WEI Y, WEN F, ZHU W, et al. Geodesic saliency using background priors[C]. Proceedings of the 12th European Conference on Computer Vision, Firenze, Italy, 2012: 29-42. 蔣寓文, 譚樂(lè)怡, 王守覺(jué). 選擇性背景優(yōu)先的顯著性檢測(cè)模型 [J]. 電子與信息學(xué)報(bào), 2015, 37(1): 130-136. doi: 10.11999/ JEIT140119. JIANG Yuwen, TAN Leyi, and WANG Shoujue. Saliency detected model based on selective edges prior[J]. Journal of Electronics Information Technology, 2015, 37(1): 130-136. doi: 10.11999/JEIT140119. WANG J, LU H, LI X, et al. Saliency detection via background and foreground seed selection[J]. Neurocomputing, 2015, 152(C): 359-368. ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282. KHAN R, VAN DE WEIJER J, KHAN F S, et al. Discriminative color descriptors[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2866-2873. JOHNSON D B. Efficient algorithms for shortest paths in sparse networks[J]. Journal of the ACM (JACM), 1977, 24(1): 1-13. OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man Cybernetics, 1979, 9(1): 62-66. HE K, SUN J, and TANG X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409. -
計(jì)量
- 文章訪問(wèn)數(shù): 1645
- HTML全文瀏覽量: 220
- PDF下載量: 513
- 被引次數(shù): 0