基于快速字典学习和特征稀有性的显著目标提取

doi:10.13229/j.cnki.jdxbgxb201605048

摘要/Abstract

摘要： 为了更准确地提取自然图像中的显著目标,本文提出了一种新的显著目标提取算法。首先,基于稀疏编码理论提出了快速字典学习算法应用于特征提取;然后,通过统计分析相应稀疏表示系数计算字典原子的稀有性,并基于特征稀有性进行显著度计算,应用数学形态学算子等进一步去除伪目标。实验结果表明:本文算法相较于其他4种现存的传统算法提取自然图像中的显著目标更为准确。此外,本文算法也能够有效地处理包含多个显著目标的自然图像。

关键词: 信息处理技术, 目标提取, 视觉注意, 快速字典学习, 特征稀有性, 受试者工作特性曲线

Abstract: In order to extract salient object in natural image more precisely, a now method to extract salient object is proposed. First, a fast dictionary learning algorithm is developed for feature extraction based on sparse coding theory. Then, the rarity of the dictionary atoms is computed by the statistical analysis of the corresponding sparse coefficients, and saliency computation is conducted based on the feature rarity. Finally, the mathematical morphology operators are applied to remove the false object. Experimental results show that the proposed method is more accurate for extracting the salient object in natural images than four existing methods and can deal with images containing multiple objects as well.

Key words: information processing technology, object extraction, visual attention, fast dictionary learning, feature rarity, receive operating characteristic(ROC) curve

中图分类号:

TN919

李蕙, 王延江, 刘宝弟, 刘伟锋, 王肖萌. 基于快速字典学习和特征稀有性的显著目标提取[J]. 吉林大学学报(工学版), 2016, 46(5): 1710-1717.

LI Hui, WANG Yan-jiang, LIU Bao-di, LIU Wei-feng, WANG Xiao-meng. Extraction algorithm of salient object by fast dictionary learning and feature rarity[J]. 吉林大学学报(工学版), 2016, 46(5): 1710-1717.

参考文献

[1] Itti L, Koch C. Computational modelling of visual attention[J]. Nature Reviews Neuroscience, 2001, 2(3): 194-203.
[2] Itti L, Koch C, 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.
[3] Treisman A M, Gelade G. A feature-integration theory of attention[J]. Cognitive Psychology, 1980, 12(1): 97-136.
[4] Koch C, Ullman S. Shifts in selective visual attention: towards the underlying neural circuitry[J]. Human Neurobiology, 1985, 4(4): 219-227.
[5] Harel J, Koch C, Perona P. Graph-based visual saliency[C]∥Advances in Neural Information Processing Systems 19, Vancouver, British Columbia, Canada, 2006: 545-552.
[6] Hou X, Zhang L. Saliency detection: a spectral residual approach[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, 2007:1-8.
[7] Li J, Levine M D, An X, et al. Visual saliency based on scale-space analysis in the frequency domain[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(4): 996-1010.
[8] Olshausen B A, Field D J. Sparse coding with an overcomplete basis set: a strategy employed by V1?[J]. Vision Research, 1997, 37(23): 3311-3325.
[9] 穆治亚, 魏仲慧, 何昕,等. 采用稀疏表示的红外图像自适应杂波抑制[J]. 光学精密工程, 2013, 21(7):1850-1857.
Mu Zhi-ya, Wei Zhong-hui, He Xin, et al. Adaptive clutter suppression of infrared images by using sparse representation[J]. Optics and Precision Engineering, 2013, 21(7):1850-1857.
[10] Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
[11] Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
[12] Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition[C]∥The Twenty-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, California, USA, 1993: 40-44.
[13] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1): 33-61.
[14] Lee H, Battle A, Raina R, et al. Efficient sparse coding algorithms[C]∥20th Annual Conference on Neural Information Processing Systems,Vancouver, British Columbia, Canada, 2006: 801-808.
[15] Liu B D, Wang Y X, Shen B, et al. Blockwise coordinate descent schemes for sparse representation[C]∥2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 2014: 5267-5271.
[16] Bertsekas D P. Nonlinear programming[J]. Journal of the Operational Research Society, 1995, 4(1): 67-110.
[17] Liu T, Yuan Z, Sun J, et al. MSRA database[DB/OL].[2015-01-06].http://research.microsoft.com/en-us/um/people/jiansun/SalientObject/salient_object.htm.
[18] Stentiford F, Park A, Heath M. An estimator for visual attention through competitive novelty with application to image compression[J]. Immunology, 2010, 104(2): 149-156.
[19] Mancas M, Mancas-thillou C, Gosselin B, et al. A rarity-based visual attention map-application to texture description[C]∥2006 International Conference on Image Processing, Atlanta, Georgia, USA, 2006: 445-448.
[20] 赵佳佳, 唐峥远, 杨杰, 等. 基于图像稀疏表示的红外小目标检测算法[J]. 红外与毫米波学报, 2011, 30(2): 156-161.
Zhao Jia-jia, Tang Zheng-yuan, Yang Jie, et al. Infrared small target detection based on image sparse representation[J]. Journal of Infrared and Millimeter Waves, 2011, 30(2): 156-161.
[21] Liu T, Yuan Z, Sun J, et al. Learning to detect a salient object[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(2): 353-367.
[22] Hemami S, Estrada F, Susstrunk S. Achanta database[DB/OL].[2015-11-04].http://ivrlwww.epfl.ch/supplementary_material/RK_CVPR09/.
[23] Yang J, Yang S, Fu Y, et al. Non-negative graph embedding[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, 2008: 1-8.

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