基于带汇点Laplace扩散模型的显著目标检测

王宝艳; 张铁; 王新刚

doi:10.11999/JEIT161296

基于带汇点Laplace扩散模型的显著目标检测

doi: 10.11999/JEIT161296

1.
(东北大学信息科学与工程学院沈阳 110819) ②(东北大学理学院沈阳 110819) ③(东北大学秦皇岛分校控制工程学院秦皇岛 066004)

基金项目:

国家自然科学基金(51475086)，辽宁省自然科学基金(2014020026)

计量
- 文章访问数: 1128
- HTML全文浏览量: 84
- PDF下载量: 307
- 被引次数: 0
出版历程
- 收稿日期: 2016-11-28
- 修回日期: 2017-04-25
- 刊出日期: 2017-08-19

Salient Object Detection Based on Laplace Diffusion Models with Sink Points

1.
(College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)
2.
(College of Science, Northeastern University, Shenyang 110819, China)

Funds:

The National Natural Science Foundation of China (51475086), The Natural Science Foundation of Liaoning Province (2014020026)

摘要

摘要: 该文基于Laplace相似度量的构造方法，针对两阶段显著目标检测中显著种子的不同类型(稀疏或稠密)，提出了相应的显著性扩散模型，从而实现了基于扩散的两阶段互补的显著目标检测。尤其是第2阶段扩散模型中汇点的融入，一方面更好地抑制了显著性图中的背景，同时对于控制因子的取值更加稳健。实验结果表明，当显著种子确定时，不同的扩散模型会导致显著性扩散程度的差异。基于带汇点Laplace的两阶段互补的扩散模型较其他扩散模型更有效、更稳健。同时，从多项评价指标分析，该算法与目前流行的5种显著目标检测算法相比，具有较大优势。这表明此种用于图像检索或分类的Laplace相似度量的构造方法在显著目标检测中也是适用的。
- 目标检测 /
- 显著性 /
- 汇点 /
- Laplace矩阵 /
- 扩散模型
Abstract: Based on Laplace similarity metrics, corresponding diffusion-based saliency models are proposed according to different clusters (sparse or dense) of salient seeds in the two-stage detection, a diffusion-based two-stage complementary method for salient object detection is therefore investigated. Especially for the introduction of sink points in the second stage, saliency maps obtained by this proposed method can well restrain background parts, as well as become more robust with the change of control factor. Experiments show that different diffusion models will cause diversities of saliency diffusion degree when salient seeds are determined. In addition, the two-stage Laplace-based diffusion model with sink points is more effective and robust than other two-stage diffusion models. Meanwhile, the proposed algorithm is superior over the existing five state-of-the-art methods in terms of different metrics. This exactly shows that the similarity metrics method applied to image retrieval and classification is also available for salient objects detection.
- Object detection /
- Saliency /
- Sink points /
- Laplace matrix /
- Diffusion model

HTML全文

参考文献(28)

SHEN Hao, LI Shuxiao, ZHU Chengfei, et al. Moving object detection in aerial video based on spatiotemporal saliency[J]. Chinese Journal of Aeronautics, 2013, 26(5): 1211-1217. doi: 10.1016/j.cja.2013.07.038.

WANG Tiantian, XIU Chunbo, and CHENG Yi. Vehicle recognition based on saliency detection and color histogram [C]. 54th IEEE Conference on Decision and Control, Osaka, Japan, 2015: 2532-2535. doi: 10.1109/CCDC.2015.7162347.

GUO Chenlei and ZHANG Liming. A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression[J]. IEEE Transactions on Image Processing, 2010, 19(1): 185-198. doi: 10.1109/TIP. 2009.2030969.

ITTI L. Automatic foveation for video compression using a neurobiological model of visual attention[J]. IEEE Transactions on Image Processing, 2004, 13(10): 1304-1318. doi: 10.1109/TIP.2004.834657.

QIN Chanchan, ZHANG Guoping, ZHOU Yicong, et al. Integration of the saliency-based seed extraction and random walks for image segmentation[J]. Neurocomputing, 2014, 129(4): 378-391. doi: 10.1016/j.neucom.2013.09.021.

LI Ang, SHE Xiaochun, and SUN Qizhi. Color image quality assessment combining saliency and FSIM[C]. Fifth International Conference on Digital Image Processing, Beijing, China, 2013: 88780I-1-88780I-5. doi: 10.1117/12. 2030719.

LI Liang, JIANG Shuqiang, ZHA Zhengjun, et al. Partial- duplicate image retrieval via saliency-guided visual matching [J]. IEEE Multimedia, 2013, 20(3): 13-23. doi: 10.1109/ MMUL.2013.15.

NA I S, LE H, KIM S H, et al. Extraction of salient objects based on image clustering and saliency[J]. Pattern Analysis and Application, 2015, 18(3): 667-675. doi: 10.1007/s10044- 015-0459-1.

HAN Jie, GUO Baolong, and SUN Wei. Target tracking method in aerial video based on saliency fusion[C]. International Conference on Mechatronics, Electronic, Industrial and Control Engineering, Shanghai, China, 2015: 723-727. doi: 10.2991/meic-15.2015.165.

BORJI A, CHENG Mingming, JIANG Huaizu, et al. Salient object detection: A benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5706-5722. doi: 10.1007/ 978-3-642-33709-3_30.

WEI Yichen, WEN Fang, ZHU Wangjiang, et al. Geodesic saliency using background priors[C]. 12th European Conference on Computer Vision, Florence, Italy, 2012: 29-42. doi: 10.1007/978-3-642-33712-3_3.

FU Keren, GU I Y H, GONG Chen, et al. Robust manifold- preserving diffusion-based saliency detection by adaptive weight construction[J]. Neurocomputing, 2015, 175: 336-347. doi: 10.1016/j.neucom.2015.10.066.

SHEN Xiaohui and WU Ying. A unified approach to salient object detection via low rank matrix recovery[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 853-860. doi: 10.1109/CVPR.2012. 6247758.

LI Xiaohui, LU Huchuan, ZHANG Lihe, et al. Saliency detection via dense and sparse reconstruction[C]. IEEE Conference on Computer Vision, Sydney, Australia, 2013: 2976-2983. doi: 10.1109/ICCV.2013.370.

YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Oregon, USA, 2013: 3166-3173. doi: 10.1109/CVPR.2013. 407.

JIANG Bowen, ZHANG Lihe, LU Huchuan, et al. Saliency detection via absorbing markov chain[C]. IEEE Conference on Computer Vision, Oregon, USA, 2013: 1665-1672. doi: 10.1109/ICCV.2013.209.

ZHOU Li, YANG Zhaohui, YUAN Qing, et al. Salient region detection via integrating diffusion-based compactness and local contrast[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3308-3320. doi: 10.1109/TIP.2015.2438546.

HWANG I, LEE S H, PARK J S, et al. Saliency detection based on seed propagation in a multi-layer graph[J]. Multimedia Tools Applications, 2017, 76(2): 2111-2129. doi: 10.1007 /s11042-015-3171-7.

GOPALAKRISHNAN V, HU Yiqun, and RAJAN D. Random walks on graphs to model saliency in images[C]. IEEE Conference on Computer Vision, Miami, USA, 2009: 1698-1705. doi: 10.1109/CVPR.2009.5206767.

FU Keren, GU I Y H, and YANG Jie. Learning full-range affinity for diffusion-based saliency detection[C]. 41st IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 2016: 1926-1930.

GONG Chen, TAO Dacheng, LIU Wei, et al. Saliency propagation from simple to difficult[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 2531-2539. doi: 10.1109/CVPR.2015.7298868.

WU Xiaoming, LI Zhenguo, and CHANG Shihfu. New insights into Laplacian similarity search[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1949-1957. doi: 10.1109/CVPR.2015. 7298805.

ACHANTA R, HEMAMI S, ESTRADA F, 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. doi: 10.1109/TPAMI. 2012.120.

OTSU N. A threshold selection method from gray level histograms[J]. IEEE Transactions on System, Man, and Cybernetic, 1979, 9(1): 62-66. doi: 10.1109/TSMC.1979. 4310076.

CHEN Shuhan, ZHENG Ling, HU Xuelong, et al. Discriminative saliency propagation with sink points[J]. Pattern Recognition, 2016, 60: 2-12. doi: 10.1016/j.patcog. 2016.05.016.

BIRJI A. What is a salient object a dataset and a baseline model for salient object detection[J]. IEEE Transactions on Image Processing, 2015, 24(2): 742-756. doi: 10.1109/TIP. 2014.2383320.

ZHU Wangjiang, LIANG Shuang, WEI Yichen, et al. Saliency optimization from robust background detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 2814-2821. doi: 10.1109/CVPR.2014.360.

ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency- tuned salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA, 2009: 1597-1604. doi: 10.1109/CVPR.2009.5206596.

施引文献

资源附件(0)

访问统计