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Salient Object Detection for Synthetic Dataset

  • Aashlesha Aswar
  • Arati ManjaramkarEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Salient object detection essentially deals with various image processing and video saliency methodologies such as object recognition, object tracking, and saliency refinement. When image contains diverse object parts with cluttered background then using background prior we perform salient object detection through which we get more accurate and robust saliency maps. This paper introduces the analysis of salient object detection using synthetic dataset which also deals with negative interference of image that contains diverse object parts with cluttered background. Earlier study uses contrast prior but nowadays researchers use mainly boundary connectivity for improving the results. So, for detecting salient object we used four stages: first, we use SLIC superpixel method for image segmentation. Second, we use boundary connectivity which distinguishes the spatial layout of image region by considering image boundaries. Third, we use background measure and for reducing the noise in both foreground and background regions. Lastly, we use optimization framework through which we acquire a clean saliency map.

Keywords

Object detection Image segmentation Image resolution Optimization 

References

  1. 1.
    Liu T, Sun J, Zheng N, Tang X, Shum H (2007) Learning to detect a salient object. In: CVPRGoogle Scholar
  2. 2.
    Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. In: CVPRGoogle Scholar
  3. 3.
    Zhu W, Ling S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: CVPRGoogle Scholar
  4. 4.
    Ma Y-F, Zhang H-J (2003) Contrast-based image attention analysis by using fuzzy growingGoogle Scholar
  5. 5.
    Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection, pp 1597–1604Google Scholar
  6. 6.
    Cheng M-M, Zhang G-X, Mitra NJ, Huang X, Hu S-M (2011) Global contrast based salient region detection, pp 409–416Google Scholar
  7. 7.
    Perazzi F, Krähenbühl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection, pp 733–740Google Scholar
  8. 8.
    Lu H, Ruan X, Yang C, Zhang L, Hsuan Yang M (2013) Saliency detection via graph-based manifold rankingGoogle Scholar
  9. 9.
    Zhu W, Wei Y, Wen F, Sun J (2012) Geodesic saliency using background priorsGoogle Scholar
  10. 10.
    Alexe B, Deselaers T, Ferrari V (2012) Measuring the objectness of image windows 34(11)Google Scholar
  11. 11.
    Zhang Z, Warrell J, Torr PHS (2011) Proposal generation for object detection using cascaded ranking svms, pp 1497–1504Google Scholar
  12. 12.
    Cheng M-M, Zhang Z, Lin W-Y, Torr PHS (2014) BING: Binarized normed gradients for objectness estimation at 300 fps. In: IEEE CVPRGoogle Scholar
  13. 13.
    Jiang P, Ling H, Yu J, Peng J (2013) Salient region detection by ufo: Uniqueness, focusness and objectness. In: ICCV’13, pp 1976–1983Google Scholar
  14. 14.
    Machiras V, Decenciere E, Walter T (2015) Spatial repulsion between markers improves watershed performance. Mathematical morphology and its applications to signal and image processing. Springer International Publishing, pp 194–202Google Scholar
  15. 15.
    Johnson DB (1977) Efficient algorithms for shortest paths in sparse networks. J ACM 24(1):1–13MathSciNetCrossRefGoogle Scholar
  16. 16.
    Alpert S, Galun M, Basri R, Brandt A (2007) Image segmentation by probabilistic bottom-up aggregation and cue integration. In: CVPRGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyS.G.G.S. I E & TNandedIndia

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