基于多特征擴散方法的顯著性物體檢測
doi: 10.11999/JEIT170827
國家自然科學基金(61671077, 61463008),福建省自然科學基金(2017J01739),福建省教育廳項目(JA15136),福建師范大學教學改革研究項目(I201602015)
Salient Object Detection via Multi-feature Diffusion-based Method
The National Natural Science Foundation of China (61671077, 61463008), The Natural Science Foundation of Fujian Province (2017J01739), The Scientific Research Fund of Fujian Education Department (JA15136), The Teaching Reform Project of Fujian Normal University (I201602015)
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摘要: 現(xiàn)有的大部分基于擴散理論的顯著性物體檢測方法只用了圖像的底層特征來構(gòu)造圖和擴散矩陣,并且忽視了顯著性物體在圖像邊緣的可能性。針對此,該文提出一種基于圖像的多層特征的擴散方法進行顯著性物體檢測。首先,采用由背景先驗、顏色先驗、位置先驗組成的高層先驗方法選取種子節(jié)點。其次,將選取的種子節(jié)點的顯著性信息通過由圖像的底層特征構(gòu)建的擴散矩陣傳播到每個節(jié)點得到初始顯著圖,并將其作為圖像的中層特征。然后結(jié)合圖像的高層特征分別構(gòu)建擴散矩陣,再次運用擴散方法分別獲得中層顯著圖、高層顯著圖。最后,非線性融合中層顯著圖和高層顯著圖得到最終顯著圖。該算法在3個數(shù)據(jù)集MSRA10K,DUT-OMRON和ECSSD上,用3種量化評價指標與現(xiàn)有4種流行算法進行實驗結(jié)果對比,均取得最好的效果。Abstract: Most existing salient object detection methods based on diffusion theory usually only use one feature of image to construct graph and diffusion matrix, and ignore the possibility that salient objects appear at the border regions of the image. In this paper, a diffusion method based on the multi-layer features of image is proposed to detect salient objects. Firstly, the seed nodes are selected by adopting the high-level prior method, which is composed of background prior, color prior, and location prior. Then, the initial saliency map is obtained by propagating the saliency information carried by the selected seed nodes to each nodes via the diffusion matrix constructed by the low-level feature of the image, and used as the middle-level feature of image. The diffusion matrices are re-synthesized again by the middle-level feature and the high-level feature of the image, and the middle-level saliency map and the high-level saliency map are obtained by the diffusion-based method respectively. The final saliency map is obtained by nonlinearly combining the the middle-level and high-level saliency map. Results on three datasets, MSRA10K, DUT-OMRON and ECSSD, show that the proposed method achieves superior performance compared with the four state-of-art methods in terms of three evaluation metrics.
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Key words:
- Salient object detection /
- Diffusion method /
- Multi-layer features /
- High-level prior
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JERRIPOTHULA K R, CAI J, and YUAN J. Image Co-segmentation via saliency Co-fusion[J]. IEEE Transactions on Multimedia, 2016, 18(9): 1896-1909. doi: 10.1109/TMM.2016.2576283. LUO P, TIAN Y, WANG X, et al. Switchable deep network for pedestrian detection[C]. IEEE Computer Vision and Pattern Recognition, Columbus, USA, 2014: 899-906. doi: 10.1109/CVPR.2014.120. ZHAO R, OUYANG W, and WANG X. Unsupervised salience learning for person re-identification[C]. IEEE Computer Vision and Pattern Recognition, Portland, Oregon, USA, 2013: 3586-3593. doi: 10.1109/CVPR.2013.460. LIU G H, YANG J Y, and LI Z Y. Content-based image retrieval using computational visual attention model[J]. Pattern Recognition, 2015, 48(8): 2554-2566. doi: 10.1016/ j.patcog.2015.02.005 唐紅梅, 吳士婧, 郭迎春, 等. 自適應閾值分割與局部背景線索結(jié)合的顯著性檢測[J]. 電子與信息學報, 2017, 39(7): 1592-1598. doi: 10.11999/JEIT160984. TANG Hongmei, WU Shijing, GUO Yingchun, et al. Saliency detection based on adaptive threshold segmentation and local background clues[J]. Journal of Electronics Information Technology, 2017, 39(7): 1592-1598. doi: 10.11999/JEIT160984. JIANG H, WANG J, YUAN Z, et al. Automatic salient object segmentation based on context and shape prior[C]. British Machine Vision Conference, Dundee, UK, 2011: 110.1-110.12. doi: 10.5244/C.25.110. WANG L, WANG L, LU H, et al. Saliency detection with recurrent fully convolutional networks[C]. European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 825-841. doi: 10.1007/978-3-319-46493-0_50. YANG J and YANG M H. Top-down visual saliency via joint CRF and dictionary learning[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2017, 39(3): 576-588. doi: 10.1109/TPAMI.2016.2547384. HU P and RAMANAN D. Bottom-up and top-down reasoning with hierarchical rectified gaussians[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 5600-5609. doi: 10.1109/CVPR.2016. 604. PERAZZI F, Krhenbhl P, PRITCH Y, et al. Saliency filters: Contrast based filtering for salient region detection[C]. IEEE Computer Vision and Pattern Recognition, Rhode Island, 2012: 733-740. doi: 10.1109/CVPR.2012. 6247743. YAN Q, XU L, SHI J, et al. Hierarchical saliency detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 1155-1162. doi: 10.1109/ CVPR.2013.153. WANG Q, ZHENG W, and PIRAMUTHU R. GraB: Visual saliency via novel graph model and background priors[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 535-543. doi: 10.1109/ CVPR.2016.64. ZHU W, LIANG S, WEI Y, 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. HAREL J, KOCH C, and PERONA P. Graph-based visual saliency[C]. International Conference on Neural Information Processing Systems, Vancouver, Canada, 2006: 545-552. doi: 10.1.1.70.2254. QIN Y, LU H, XU Y, et al. Saliency detection via cellular automata[C]. IEEE Computer Vision and Pattern Recognition, Boston, USA, 2015: 110-119. doi: 10.1109/ CVPR.2015.7298606. YU J G, XIA G S, GAO C, et al. A computational model for object-based visual saliency: Spreading attention along gestalt cues[J]. IEEE Transactions on Multimedia, 2016, 18(2): 273-286. doi: 10.1109/TMM.2015.2505908. YANG C, ZHANG L, LU H, et al. Saliency detection via graph-based manifold ranking[C]. IEEE Computer Vision and Pattern Recognition, Portland, USA, 2013: 3166-3173. doi: 10.1109/CVPR.2013.407. JIANG P, VASCONCELOS N, and PENG J. Generic promotion of diffusion-based salient object detection[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 217-225. doi: 10.1109/ICCV.2015.33. ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2012, 34(11): 2274-2282. doi: 10.1109/TPAMI.2012.120. LUXBURG U V. A tutorial on spectral clustering[J]. Statistics and Computing, 2007, 17(4): 395-416. doi: 10.1007/ s11222-007-9033-z. PENG H, LI B, LING H, et al. Salient object detection via structured matrix decomposition[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2017, 39(4): 818-832. doi: 10.1109/TPAMI.2016.2562626. WU Y. A unified approach to salient object detection via low rank matrix recovery[C]. IEEE Computer Vision and Pattern Recognition, Rhode Island, 2012: 853-860. doi: 10.1109/ CVPR.2012.6247758. ZHANG Lihe, YANG C, LU H, et al. Ranking saliency[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(9): 1892-1904. doi: 10.1109/TPAMI. 2016.2609426. Borji A, CHENH Mingming, JIANG Huaizu, et al. Salient object detection: A benchmark[C]. IEEE Computer Vision and Pattern Recognition, Boston, USA, 2015: 5706-5722. doi: 10.1109/TIP.2015.2487833. -
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