Abstract
Salient Object Detection (SOD) has several applications including image and video compression, video summarization, image segmentation and object discovery etc. Several Methods have been suggested in literature for detecting salient object in digital images. Most of these methods aim at detecting salient objects in images which does not contain any artifact such as noise. In this paper, we have evaluated several salient object detection methods in noisy environment on publicly available ASD Dataset. The performance of the methods is evaluated in terms of Precision, Recall and F-measure and Area under the curve (AUC). It has been observed that there is no clear winner but the methods proposed by Liu et al. and Harel et al. are better in comparison to other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kanan, C., Cottrell, G.: Robust classification of objects, faces, and flowers using natural image statistics. In: Proceedings of the IEEE Computer Scoiecty Conference on Computer Vision and Pattern Recognition, pp. 2472–2479 (2010)
Ren, Z., Gao, S., Chia, L.-T., Tsang, I.: Region-based saliency detection and its application in object recognition. IEEE TCSVT 24, 769–779 (2013)
Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE TIP 19, 185–198 (2010)
Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE TIP 13, 1304–1318 (2004)
Ma, Y.-F., Hua, X.-S., Lu, L., Zhang, H.-J.: A generic framework of user attention model and its application in video summarization. IEEE TMM 7, 907–919 (2005)
Donoser, M., Urschler, M., Hirzer, M., Bischof, H.: Saliency driven total variation segmentation. In: ICCV, pp. 817–824 (2009)
Karpathy, A., Miller, S., Fei-Fei, L.: Object discovery in 3d scenes via shape analysis. In: Proceedings of the International Conference on Robotics and Automation, pp. 2088–2095 (2013)
Frintrop, S., Garca, G.M., Cremers, A.B.: A cognitive approach for object discovery. In: Proceedings of the International Conference on Pattern Recognition, pp. 2329–2334 (2014)
Chen, T., Cheng, M.-M., Tan, P., Shamir, A., Hu, S.-M.: Sketch2photo: internet image montage. ACM TOG 28, 124:1–124:10 (2009)
Sun, J., Xie, J., Liu, J., Sikora, T.: Image adaptation and dynamic browsing based on two-layer saliency combination. IEEE Trans. Broadcast. 59, 602–613 (2013)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trnas. PAMI 20, 1254–1259 (1998)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)
Achanta, R., Susstrunk, S.: Saliency detection using maximum symmetric surround. In: Proceedings of the 17th IEEE International Conference on Image Processing (ICIP), pp. 2653–2656 (2010)
Bruce, N.D., Tsotsos, J.K.: Saliency based on information maximization. In: Proceedings of NIPS, pp. 155–162 (2005)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of NIPS, pp. 545–552 (2007)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE TPAMI 34, 1915–1926 (2012)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proceddings of the IEEE International Conference on CVPR, pp. 1–8 (2007)
Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.-Y.: Learning to detect a salient object. In: Proceedings of the IEEE International Conference on Computer Vision Pattern Recognition, pp. 1–8 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kumar, N., Singh, M., Govil, M.C., Pilli, E.S., Jaiswal, A. (2016). Salient Object Detection in Noisy Images. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-34111-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-34110-1
Online ISBN: 978-3-319-34111-8
eBook Packages: Computer ScienceComputer Science (R0)