Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending

Wei LI; Quanlong LI; Zhengyi LIU

doi:10.11999/JEIT190093

Volume 41 Issue 10

Oct. 2019

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(10): 2442-2449

Wei LI, Quanlong LI, Zhengyi LIU. Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2442-2449. doi: 10.11999/JEIT190093

Citation:

Wei LI, Quanlong LI, Zhengyi LIU. Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2442-2449. doi: 10.11999/JEIT190093

Citation:

PDF( 2232 KB)

Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending

doi: 10.11999/JEIT190093

College of Computer Science and Technology, Anhui University, Hefei 230601, China

Received Date: 2019-02-01
Rev Recd Date: 2019-06-03

Available Online: 2019-06-12

Publish Date: 2019-10-01

Abstract

Abstract

Salient object detection which aims at automatically detecting what attracts human’s attention most in a scene, bootstrap learning based on Support Vector Machine(SVM) has achieved excellent performance in bottom-up methods. However, it is time-consuming for each image to be trained once based on multiple kernel SVM ensemble. So a salient object detection model via Weighted K-Nearest Neighbor Linear Blending (WKNNLB) is proposed. First of all, existing saliency detection methods are employed to generate weak saliency maps and obtain training samples. Then, Weighted K-Nearest Neighbor (WKNN) is introduced to learning salient score of samples. WKNN model needs no pre-training process, only needs selecting K value and computing saliency value by the K-nearest neighbors labels of training sample and the distances between the K-nearest neighbors training samples and the testing sample. In order to reduce the influence of selecting K value, linear blending of multi-WKNNs is applied to generating strong saliency maps. Finally, multi-scale saliency maps of weak and strong model are integrated together to further improve the detection performance. The experimental results on common ASD and complex DUT-OMRON datasets show that the algorithm is effective and superior in running time and performance. It can even perform favorable against the state-of-the-art methods when adopting better weak saliency map.
- Salient object detection,
- Bootstrap learning,
- Linear blending,
- Weighted K-Nearest Neighbor(WKNN)

FullText(HTML)

References(23)

References

BORJI A and ITTI L. State-of-the-art in visual attention modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 185–207. doi: 10.1109/TPAMI.2012.89

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

ZHANG Fan, DU Bo, and ZHANG Liangpei. Saliency-guided unsupervised feature learning for scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2175–2184. doi: 10.1109/TGRS.2014.2357078

LU Xiaoqiang, ZHENG Xiangtao, and LI Xuelong. Latent semantic minimal hashing for image retrieval[J]. IEEE Transactions on Image Processing, 2017, 26(1): 355–368. doi: 10.1109/TIP.2016.2627801

WEI Yunchao, XIAO Huaxin, SHI Honghui, et al. Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7268–7277. doi: 10.1109/CVPR.2018.00759.

ZHANG Xiaoning, WANG Tiantian, QI Jinqing, et al. Progressive attention guided recurrent network for salient object detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 714–722. doi: 10.1109/CVPR.2018.00081.

CHEN Shuhan, TAN Xiuli, WANG Ben, et al. Reverse attention for salient object detection[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 236–252. doi: 10.1007/978-3-030-01240-3_15.

ZHANG Lu, DAI Ju, LU Huchuan, et al. A bi-directional message passing model for salient object detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 1741–750. doi: 10.1109/CVPR.2018.00187.

WANG Tiantian, ZHANG Lihe, WANG Shuo, et al. Detect globally, refine locally: A novel approach to saliency detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3127–3135. doi: 10.1109/CVPR.2018.00330.

HOU Qibin, CHENG Mingming, HU Xiaowei, et al. Deeply supervised salient object detection with short connections[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(4): 815–828. doi: 10.1109/TPAMI.2018.2815688

YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3166–3173. doi: 10.1109/CVPR.2013.407.

CHENG Mingming, WARRELL J, LIN Wenyan, et al. Efficient salient region detection with soft image abstraction[C]. 2013 IEEE International Conference on Computer Vision, Sydney, Australia, 2013: 1529–1536. doi: 10.1109/ICCV.2013.193.

ZHANG Jianming, SCLAROFF S, LIN Zhe, et al. Minimum barrier salient object detection at 80 FPS[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1404–1412. doi: 10.1109/ICCV.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: 10.1109/TIP.2015.2487833

TONG Na, LU Huchuan, RUAN Xiang, et al. Salient object detection via bootstrap learning[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1884–1892. doi: 10.1109/CVPR.2015.7298798.

LU Huchuan, ZHANG Xiaoning, Qi Jinqing, et al. Co-bootstrapping saliency[J]. IEEE Transactions on Image Processing, 2017, 26(1): 414–425. doi: 10.1109/TIP.2016.2627804

SONG Hangke, LIU Zhi, DU Huan, et al. Depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning[J]. IEEE Transactions on Image Processing, 2017, 26(9): 4204–4216. doi: 10.1109/TIP.2017.2711277

WU Lishan, LIU Zhi, SONG Hangke, et al. RGBD co-saliency detection via multiple kernel boosting and fusion[J]. Multimedia Tools and Applications, 2018, 77(16): 21185–21199. doi: 10.1007/s11042-017-5576-y

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. doi: 10.1109/TPAMI.2012.120

OJALA T, PIETIKAINEN M, and MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971–987. doi: 10.1109/tpami.2002.1017623

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

ITTI L, KOCH C, and 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. doi: 10.1109/34.730558

SHEN Xiaohui and WU Ying. A unified approach to salient object detection via low rank matrix recovery[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 853–860. doi: 10.1109/CVPR.2012.6247758.

Relative Articles

Supplements(0)

Cited By

Proportional views