Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

@article{Li2013VisualSB,
  title={Visual Saliency Based on Scale-Space Analysis in the Frequency Domain},
  author={Jian Li and Martin D. Levine and Xiangjing An and Xin Xu and Hangen He},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2013},
  volume={35},
  pages={996-1010}
}
  • Jian Li, M. Levine, Hangen He
  • Published 1 April 2013
  • Physics
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
We address the issue of visual saliency from three perspectives. First, we consider saliency detection as a frequency domain analysis problem. Second, we achieve this by employing the concept of nonsaliency. Third, we simultaneously consider the detection of salient regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of natural images. We show that the convolution of the image… 
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References

SHOWING 1-10 OF 52 REFERENCES
A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression
TLDR
Extensive tests of videos, natural images, and psychological patterns show that the proposed PQFT model is more effective in saliency detection and can predict eye fixations better than other state-of-the-art models in previous literature.
Saliency Detection: A Spectral Residual Approach
TLDR
A simple method for the visual saliency detection is presented, independent of features, categories, or other forms of prior knowledge of the objects, and a fast method to construct the corresponding saliency map in spatial domain is proposed.
Bottom-up saliency is a discriminant process
TLDR
The resulting saliency detector is shown to replicate the fundamental properties of the psychophysics of pre-attentive vision, including stimulus pop-out, inability to detect feature conjunctions, asymmetries with respect to feature presence vs. absence, and compliance with Weber's law.
SUN: A Bayesian framework for saliency using natural statistics.
TLDR
In the model, saliency is computed locally, which is consistent with the neuroanatomy of the early visual system and results in an efficient algorithm with few free parameters, which provides a straightforward explanation for many search asymmetries observed in humans.
Spatiotemporal Saliency in Dynamic Scenes
TLDR
The algorithm is inspired by biological mechanisms of motion-based perceptual grouping and extends a discriminant formulation of center-surround saliency previously proposed for static imagery, and yields a robust, versatile, and fully unsupervised spatiotemporal saliency algorithm, applicable to scenes with highly dynamic backgrounds and moving cameras.
The discriminant center-surround hypothesis for bottom-up saliency
TLDR
In result, discriminant saliency is shown to predict eye fixations better than previous models, and produces background subtraction algorithms that outperform the state-of-the-art in computer vision.
On the plausibility of the discriminant center-surround hypothesis for visual saliency.
TLDR
The discriminant saliency detectors outperform previously proposed methods from both the saliency and the general computer vision literatures and make accurate quantitative predictions of various psychophysics of human saliency for both static and motion stimuli.
A Nonparametric Approach to Bottom-Up Visual Saliency
TLDR
The model is rather simplistic and essentially parameter-free, and contrasts recent developments in the field that usually aim at higher prediction rates at the cost of additional parameters and increasing model complexity, and in fact learns image features that resemble findings from several previous studies.
Video processing with scale-aware saliency: Application to Frame Rate Up-Conversion
  • N. Jacobson, Truong Q. Nguyen
  • Computer Science
    2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2011
TLDR
The ability of the algorithm to detect salient objects at multiple scales allows for class-leading performance both objectively in terms of PSNR/SSIM as well as subjectively.
Frequency-tuned salient region detection
TLDR
This paper introduces a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects that outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.
...
...