稀疏重构和紧凑性结合的图像显著性检测

doi:10.3778/j.issn.1673-9418.1910016

摘要/Abstract

摘要：

针对复杂环境下，当前图像显著性检测算法难以正确检测显著物体的问题，提出结合稀疏重构误差和图像显著区域紧凑性计算图像显著性的方法。首先提取图像中的主结构以弱化背景噪声，并将处理后的图像分割成若干超像素。一方面利用边界超像素构建背景字典，将各个超像素投影在该字典上进行稀疏重构，利用重构误差得到基于稀疏重构的显著图；另一方面利用图像中显著物体的紧凑性分别计算基于前景、背景种子的显著图并融合。最后将由稀疏重构误差和紧凑性得到的显著图融合得到最终的显著图。在多个公开数据集上，将所提算法与近些年提出的13种算法进行对比实验，实验结果显示提出的算法优于所有对比算法。

关键词: 显著性, 稀疏重构, 紧凑性, 结构提取

Abstract:

Aiming at the problem that existing image saliency detection algorithms can??t correctly detect salient objects in complex environments, this paper proposes a method combining sparse reconstruction error and the compactness of image salient regions to calculate image saliency. Firstly, the main structure of the image is extracted to reduce background noise. Then the processed image is segmented into several superpixels. On one hand, the background dictionary is constructed by using the boundary superpixels. Each superpixel is projected on the dictionary for sparse reconstruction, and the reconstruction error is used to obtain a saliency map based on sparse reconstruction. On the other hand, the maps based on foreground seeds and background seeds are calculated separately via compactness of salient objects and fused. Finally, the saliency map obtained by the sparse reconstruction error and compactness are fused to get the final saliency map. The proposed algorithm is compared with 13 algorithms proposed in recent years on several public datasets. The experimental results show that the proposed algorithm is superior to all comparison algorithms.

Key words: saliency, sparse reconstruction, compactness, structure extraction

张莹莹，葛洪伟. 稀疏重构和紧凑性结合的图像显著性检测[J]. 计算机科学与探索, 2020, 14(12): 2122-2131.

ZHANG Yingying, GE Hongwei. Image Saliency Detection Combining Sparse Reconstruction and Compactness[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(12): 2122-2131.

参考文献

[1] Yuan Y, Li C, Kim J, et al. Reversion correction and regula-rized random walk ranking for saliency detection[J]. IEEE Transactions on Image Processing, 2018, 27(3): 1311-1322.
[2] Zhao X C, Liu Z Y, Li W. Co-saliency detection via saliency propagation and constraint[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(11): 1925-1934. 赵悉超, 刘政怡, 李炜. 图像显著性传播及约束的协同显著性检测[J]. 计算机科学与探索, 2019, 13(11): 1925-1934.
[3] Wang W, Shen J, Yang R, et al. Saliency-aware video object segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(1): 20-33.
[4] Wang Q, Tang S, Zhai D, et al. Salience based object tracking in complex scenes[J]. Neurocomputing, 2018, 314: 132-142.
[5] Wang J, Lu H, Li X, et al. Saliency detection via background and foreground seed selection[J]. Neurocomputing, 2015, 152: 359-368.
[6] Zhou L, Yang Z, Yuan Q, et al. Salient region detection via integrating diffusion-based compactness and local contrast[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3308-3320.
[7] Tong N, Lu H, Zhang L, et al. Saliency detection with multi-scale superpixels[J]. IEEE Signal Processing Letters, 2014, 21(9): 1035-1039.
[8] Jiang B W, Zhang L H, Lu H C, et al. Saliency detection via absorbing Markov chain[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, Dec 1-8, 2013. Washington: IEEE Computer Society, 2013: 1665- 1672.
[9] Zhao Q, Wang A P, Liu Z Y. RGB-D image saliency detection via background and foreground fusion[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(7): 1232-1242. 赵强, 王爱平, 刘政怡. 背景与前景融合的RGB-D图像显著性检测[J]. 计算机科学与探索, 2020, 14(7): 1232-1242.
[10] Qin Y, Lu H C, Xu Y Q, et al. Saliency detection via cellular automata[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, Jun 7-12, 2015. Washington: IEEE Computer Society, 2015: 110-119.
[11] Yang C, Zhang L H, Lu H C, et al. Saliency detection via graph-based manifold ranking[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, Jun 23-28, 2013. Washington: IEEE Computer Society, 2013: 3166-3173.
[12] Zhou L, Yang Z H, Zhou Z T, et al. Salient region detection using diffusion process on a two-layer sparse graph[J]. IEEE Transactions on Image Processing, 2017, 26(12): 5882-5894.
[13] Li X H, Lu H C, Zhang L H, et al. Saliency detection via dense and sparse reconstruction[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, Dec 1-8, 2013. Washington: IEEE Computer Society, 2013: 2976-2983.
[14] Huo L, Yang S, Jiao L, et al. Local graph regularized sparse reconstruction for salient object detection[J]. Neurocomputing, 2016, 194: 348-359.
[15] Jia L, Zhang J, Chen S, et al. Fabric defect inspection based on lattice segmentation and lattice templates[J]. Journal of the Franklin Institute, 2018, 355(15): 7764-7798.
[16] 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.
[17] Zhu W J, Liang S, Wei Y C, et al. Saliency optimization from robust background detection[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Jun 23-28, 2014. Washington: IEEE Computer Society, 2014: 2814-2821.
[18] Cheng M M, Mitra N J, Huang X, et al. Global contrast based salient region detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 569-582.
[19] Shi J, Yan Q, Xu L, et al. Hierarchical image saliency detection on extended CSSD[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(4): 717-729.
[20] Li Y, Hou X D, Koch C, et al. The secrets of salient object segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Jun 23-28, 2014. Washington: IEEE Computer Society, 2014: 280-287.
[21] Movahedi V, Elder J H. Design and perceptual validation of performance measures for salient object segmentation[C]//Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, San Francisco, Jun 13-18, 2010. Washington: IEEE Computer Society, 2010: 49-56.
[22] Fan D P, Gong C, Cao Y, et al. Enhanced-alignment measure for binary foreground map evaluation[J]. arXiv:1805.10421, 2018.
[23] Sun J, Lu H, Liu X. Saliency region detection based on Markov absorption probabilities[J]. IEEE Transactions on Image Processing, 2015, 24(5): 1639-1649.
[24] Li H, Lu H, Lin Z, et al. Inner and inter label propagation: salient object detection in the wild[J]. IEEE Transactions on Image Processing, 2015, 24(10): 3176-3186.
[25] Zhu X, Tang C, Wang P, et al. Saliency detection via affinity graph learning and weighted manifold ranking[J]. Neurocomputing, 2018, 312: 239-250.
[26] Li C Y, Yuan Y C, Cai W D, et al. Robust saliency detection via regularized random walks ranking[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, Jun 7-12, 2015. Washington: IEEE Computer Society, 2015: 2710-2717.