Target Segmentation of Infrared Image Using Fused Saliency Map and Efficient Subwindow Search
-
摘要: 为了快速精确地分割红外图像目标，提出一种基于融合显著图和高效子窗口搜索的红外目标分割方法.在获取图像超像素的基础上，提取每个区域增强的Sigma特征，并考虑邻域对比度、背景对比度、空间距离和区域大小的影响，构建局部显著图，接着利用全局核密度估计构建全局显著图，然后融合局部和全局显著图实现图像显著性检测，最后应用高效子窗口搜索方法检测和筛选目标，实现红外目标分割.实验结果表明，新方法的显著图结果目标区域一致高亮且边缘清晰，背景杂波抑制效果好，可实现快速精确的目标分割.Abstract: In order to segment infrared target fast and precisely, a segmentation method using fused saliency map and efficient subwindow search is proposed. Firstly, based on image superpixels, the enhanced sigma feature of every region is extracted, and at the same time considering the influence of neighbor contrast, background contrast, spatial distance and region's size, the local saliency map is constructed. Next, the global saliency map is obtained by global kernel density estimation. Then, the saliency detection result of infrared image is obtained by fusing the local and global saliency maps. Finally, the efficient subwindow search method is used to detect and segment the target. Experimental results show that the saliency map of the proposed method has complete target feature, obvious edges and good background suppression, and that the algorithm can segment the infrared target fast and precisely.1) 本文责任编委 刘跃虎
-
[1] 刘松涛, 杨绍清.基于元胞自动机的红外弱小目标图像分割.红外与毫米波学报, 2008, 27(1):42-46 doi: 10.3321/j.issn:1001-9014.2008.01.010Liu Song-Tao, Yang Shao-Qing. Segmentation of infrared weak and small target image based on cellular automata. Journal of Infrared and Millimeter Waves, 2008, 27(1):42-46 doi: 10.3321/j.issn:1001-9014.2008.01.010 [2] Rajchl M, Lee M C H, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W J, et al. DeepCut:object segmentation from bounding box annotations using convolutional neural networks. IEEE Transactions on Medical Imaging, 2016, 36(2):674-683 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ029696105/ [3] Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11):1254-1259 doi: 10.1109/34.730558 [4] Ma Y F, Zhang H J. Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the 11th ACM International Conference on Multimedia. Berkeley, CA, USA: ACM, 2003. 374-381 http://www.mendeley.com/catalog/contrastbased-image-attention-analysis-using-fuzzy-growing/ [5] Hou X D, Zhang L Q. Saliency detection: a spectral residual approach. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA: IEEE, 2007. 1-8 http://www.mendeley.com/catalog/saliency-detection-spectral-residual-approach/ [6] Guo C L, Ma Q, Zhang L M. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA: IEEE, 2008. 1-8 http://www.mendeley.com/catalog/spatiotemporal-saliency-detection-using-phase-spectrum-quaternion-fourier-transform/ [7] Jung C, Kim C. A unified spectral-domain approach for saliency detection and its application to automatic object segmentation. IEEE Transactions on Image Processing, 2012, 21(3):1272-1283 doi: 10.1109/TIP.2011.2164420 [8] He X, Jing H Y, Han Q, Niu X M. Salient region detection combining spatial distribution and global contrast. Optical Engineering, 2012, 51(4):Article No. 047007 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0226257555/ [9] Zhang Y B, Han J W, Guo L. Image saliency detection based on histogram. Journal of Computational Information Systems, 2014, 10(6):2417-2424 [10] Xu L F, Li H L, Zeng L Y, Ngan K N. Saliency detection using joint spatial-color constraint and multi-scale segmentation. Journal of Visual Communication and Image Representation, 2013, 24(4):465-476 doi: 10.1016/j.jvcir.2013.02.007 [11] Liu S T, Shen T S, Dai Y. Infrared image segmentation method based on spatial coherence histogram and maximum entropy. In: Proceedings of the 2014 SPIE 9275, Infrared, Millimeter-Wave, and Terahertz Technologies Ⅲ. Beijing, China: SPIE, 2014. 1-8 http://www.deepdyve.com/lp/spie/infrared-image-segmentation-method-based-on-spatial-coherence-JDWSMYXGT0 [12] Li J, Levine M D, Aa X J, He H G. Saliency detection based on frequency and spatial domain analyses. In: Proceedings of the 2011 British Machine Vision Conference. Dundee, Scotland, UK: BMVA, 2011. 1-11 [13] Cheng M M, Zhang G X, Mitra N J, Huang X L, Hu S M. Global contrast based salient region detection. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Providence, Rhode Island, USA: IEEE, 2011. 409-416 https://www.ncbi.nlm.nih.gov/pubmed/26353262 [14] Perazzi F, Krähenbuhl P, Pritch Y, Hornung A. Saliency filters: contrast based filtering for salient region detection. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, Rhode Island, USA: IEEE, 2012. 733-740 http://dl.acm.org/citation.cfm?id=2355041 [15] Liu T, Sun J, Zheng N N, Tang X O, Shum H Y. Learning to detect a salient object. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA: IEEE, 2007. 1-8 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=4270072 [16] Yang J M, Yang M H. Top-down visual saliency via joint CRF and dictionary learning. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Rhode Island, USA: IEEE, 2012. 2296-2303 https://www.mendeley.com/catalogue/topdown-visual-saliency-via-joint-crf-dictionary-learning/ [17] Kocak A, Cizmeciler K, Erdem A, Erdem E. Top down saliency estimation via superpixel-based discriminative dictionaries. In: Proceedings of the 2014 British Machine Vision Conference. Nottingham, UK: BMVA, 2014. 1-12 [18] Jiang H Z, Wang J D, Yuan Z J, Wu Y, Zheng N N, Li S P. Salient object detection: a discriminative regional feature integration approach. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, Oregon, USA: IEEE, 2013. 2083-2090 doi: 10.1007/s11263-016-0977-3 [19] Zhang L, Tong M H, Marks T K, Shan H, Cottrell G W. SUN:a Bayesian framework for saliency using natural statistics. Journal of Vision, 2008, 8(7):Article No. 32 doi: 10.1167/8.7.32 [20] Cholakkal H, Johnson J, Rajan D. Backtracking ScSPM image classifier for weakly supervised top-down saliency. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016. 5278-5287 http://ieeexplore.ieee.org/document/7780939/ [21] Peng H P, Li B, Ling H B, Hu W M, Xiong W H, Maybank S J. Salient object detection via structured matrix decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):818-832 doi: 10.1109/TPAMI.2016.2562626 [22] Lampert C H, Blaschko M B, Hofmann T S. Beyond sliding windows: object localization by efficient subwindow search. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA: IEEE, 2008. 1-8 [23] Ye L, Yuan J S, Xue P, Tian Q. Saliency density maximization for object detection and localization. In: Proceedings of the 10th Asian Conference on Computer Vision. Queenstown, New Zealand: Springer-Verlag, 2010. 396-408 https://www.mendeley.com/catalogue/saliency-density-maximization-object-detection-localization/ [24] Gidaris S, Komodakis N. LocNet: improving localization accuracy for object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016. 789-798 [25] Achanta R, Hemami S, Estrada F, Susstrunk S. Frequency-tuned salient region detection. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE, 2009. 1597-1604 http://www.mendeley.com/catalog/frequencytuned-salient-region-detection/ [26] Liu Z, Shi R, Shen L Q, Xue Y Z, Ngan K N, Zhang Z Y. Unsupervised salient object segmentation based on kernel density estimation and two-phase graph cut. IEEE Transactions on Multimedia, 2012, 14(4):1275-1289 doi: 10.1109/TMM.2012.2190385 [27] Erdem E, Erdem A. Visual saliency estimation by nonlinearly integrating features using region covariances. Journal of Vision, 2013, 13(4):Article No. 11 doi: 10.1167/13.4.11 [28] Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2274-2282 doi: 10.1109/TPAMI.2012.120 [29] Tuzel O, Porikli F, Meer P. Region covariance: a fast descriptor for detection and classification. In: Proceedings of the 9th European Conference on Computer Vision. Graz, Austria: Springer, 2006. 589-600 doi: 10.1007/11744047_45 [30] 刘松涛, 常春, 沈同圣.基于区域协方差的图像特征融合方法.电光与控制, 2015, 22(2):7-11, 16 doi: 10.3969/j.issn.1671-637X.2015.02.002Liu Song-Tao, Chang Chun, Shen Tong-Sheng. An image feature fusion method based on region covariance. Electronics Optics and Control, 2015, 22(2):7-11, 16 doi: 10.3969/j.issn.1671-637X.2015.02.002 [31] Hong X P, Chang H, Shan S G, Chen X L, Gao W. Sigma set: a small second order statistical region descriptor. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL: USA, 2009. 1802-1809 http://www.mendeley.com/catalog/sigma-set-small-second-order-statistical-region-descriptor/ [32] 刘松涛, 黄金涛, 刘振兴.基于显著图生成和显著密度最大化的高效子窗口搜索目标检测方法.电光与控制, 2015, 22(12):9-14 http://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201512002.htmLiu Song-Tao, Huang Jin-Tao, Liu Zhen-Xing. An ESS target detection method based on itti's saliency map and maximum saliency density. Electronics Optics and Control, 2015, 22(12):9-14 http://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201512002.htm [33] Harel J, Koch C, Perona P. Graph-based visual saliency. In: Proceedings of the 2006 Conference Advances in Neural Information Processing Systems. Vancouver, Canada: MIT, 2006. 545-552 https://ieeexplore.ieee.org/document/6287326?reload=true&arnumber=6287326 [34] Li J, Levine M D, Aa X J, Xu X, He H G. Visual saliency based on scale-space analysis in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(4):996-1010 doi: 10.1109/TPAMI.2012.147 [35] Achanta R, Süsstrunk S. Saliency detection using maximum symmetric surround. In: Proceedings of the 17th IEEE International Conference on Image Processing. Hong Kong, China: IEEE, 2010. 2653-2656 https://www.mendeley.com/catalogue/saliency-detection-using-maximum-symmetric-surround/ [36] Rahtu E, Kannala J, Salo M. Segmenting salient objects from images and videos. In: Proceedings of the 11th European Conference on Computer Vision: Part V. Crete, Greece: Springer, 2010. 366-379 https://www.mendeley.com/catalogue/segmenting-salient-objects-images-videos/ [37] Hou X D, Harel J, Koch C. Image signature:highlighting sparse salient regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(1):194-201 doi: 10.1109/TPAMI.2011.146 [38] Murray N, Vanrell M, Otazu X, Parraga C A. Saliency estimation using a non-parametric low-level vision model. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Providence, Rhode Island, USA: IEEE, 2011. 433-440 https://www.mendeley.com/catalogue/saliency-estimation-using-nonparametric-lowlevel-vision-model/ [39] Alpert S, Galun M, Basri R, Brandt A. Image segmentation by probabilistic bottom-up aggregation and cue integration. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA: IEEE, 2007. 1-8 https://www.ncbi.nlm.nih.gov/pubmed/21690639 -
下载:
图(7) / 表(1)
计量
- 文章访问数: 1935
- HTML全文浏览量: 274
- PDF下载量: 457
- 被引次数: 0
