The Two Worlds of Saliency

Fixation Prediction

To compute a probabilistic map of an image to predict the actual human eye gaze patterns

  • ITTI [Itti et al. PAMI 98]
  • AIM [Bruce et al. NIPS 06]
  • GBVS [Harel et al. NIPS 07]
  • DVA [Hou et al. NIPS 08]
  • SUN [Zhang et al. JOV 08]
  • SIG [Hou et al. PAMI 12]

Salient Object Segmentation

To generate masks that matches the annotated silhouettes of salient objects

  • FT [Achanta et al. CVPR 09]
  • GC [Cheng et al. CVPR 11]
  • SF [Perazzi et al. CVPR 12]
  • PCA-S [Margolin et al. CVPR 13]
  • And more ...

The PASCAL-S Dataset

Bridge the gap between fixations and salient objects

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Original Image

Images from the validation set of PASCAL VOC 2010

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Full Segmentation

Each image is manually segmented for salient object annotation

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Eye Fixations

Eye tracking from multiple subjects during a two-second free viewing

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Salient Object Masks

Salient objects selected by multiple subjects using the segmentations

Images from PASCAL 2010
Object Instances


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Left: Fixation Prediction results on PASCAL-S, BRUCE [2], Cerf [9], IS [10] and Judd [11] with 7 different algorithms and human consistency. Algorithms include AWS [14], AIM [2], SIG [5], DVA [6], GBVS [4], SUN [13] and ITTI [7].

Right Salient object segmentation results on FT [1], IS [10] and PASCAL-S with 4 different algorithms and human consistency. Algorithms include SF [9], PCAS [12], GC [3] and FT [1].

Our Findings

  • The definition of Salient Objects is an intrinsic property of the image, which can be reliably perceived among human subjects.

    The Definition of Salient Objects
  • Unlike fixation datasets, the most widely used salient object segmentation dataset is heavily biased.

    Dataset Design Bias
  • There exists a strong correlation between fixations and salient objects, which can be used to improve salient object segmentation.

    A Novel Paradigm for Salient Object Segmentation

Our Method

We propose a salient object segmentation model by combining object proposal and fixation prediction

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Our core idea is to first generate a set of object candidates, and then use the fixation algorithms to rank different regions based on their saliency.


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The F-measures of all algorithms on PASCAL-S, IS [10] and FT [1] dataset. All CPMC+Fixation results are obtained using top K = 20 segments. Our method with GBVS [4] outperformed state-of-the-art methods on salient object segmentation.


  1. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. CVPR 2009
  2. N. Bruce and J. Tsotsos. Saliency based on information maximization. NIPS 2005
  3. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu. Global contrast based salient region detection. CVPR 2011
  4. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS 2006
  5. X. Hou, J. Harel, and C. Koch. Image signature: Highlighting sparse salient regions. TPAMI 2012
  6. X. Hou and L. Zhang. Dynamic visual attention: Searching for coding length increments. NIPS 2008
  7. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. TPAMI 1998
  8. F. Perazzi, P. Krahenbuhl, Y. Pritch, and A. Hornung. Saliency filters: Contrast based filtering for salient region detection. CVPR 2012
  9. M. Cerf, J. Harel, W. Einhauser, and C. Koch. Predicting human gaze using low-level saliency combined with face detection. NIPS 2008
  10. T. Judd, K. Ehinger, F. Durand, and A. Torralba. Learning to predict where humans look. ICCV 2009
  11. J. Li, M. D. Levine, X. An, X. Xu, and H. He. Visual saliency based on scale-space analysis in the frequency domain. TPAMI 2013
  12. R. Margolin, A. Tal, and L. Zelnik-Manor. What makes a patch distinct? CVPR 2013
  13. L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G. W. Cottrell. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision 2012
  14. A. Garcia-Diaz, V. Leboran, X. R. Fdez-Vidal, and X. M. Pardo. On the relationship between optical variability, visual saliency, and eye fixations: A computational approach. Journal of Vision 2008


Dataset & Code


A joint effort from Georgia Tech, Caltech and UCLA

Meet the authors

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Yin Li

PhD Student
Georgia Tech

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Xiaodi Hou

PhD @ Caltech

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Christof Koch

Chief Scientific Officer
of the Allen Institute for Brain Science

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James M. Rehg

Professor @ Georgia Tech

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Alan L. Yuille

Professor @ UCLA

Contact Us

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