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

arXiv: https://arxiv.org/abs/1605.01999 · HTML: https://mlsanity.com/paper/1605.01999/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2016.

## Built on by (most cited first, 11 of 11)

- [Saliency in Augmented Reality](https://mlsanity.com/paper/2204.08308/) (2022)
- [A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification](https://mlsanity.com/paper/2204.04462/) (2022)
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- [How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model](https://mlsanity.com/paper/2408.05411/) (2024)
- [Predicting Eye Gaze Location on Websites](https://mlsanity.com/paper/2211.08074/) (2022)
- [Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images](https://mlsanity.com/paper/2506.21925/) (2025)
- [Adversarial Attacks Assessment of Salient Object Detection via Symbolic Learning](https://mlsanity.com/paper/2309.05900/) (2023)
- [UHKD: A Unified Framework for Heterogeneous Knowledge Distillation via Frequency-Domain Representations](https://mlsanity.com/paper/2510.24116/) (2025)
- [Deriving Explanation of Deep Visual Saliency Models](https://mlsanity.com/paper/2109.03575/) (2021)
- [Interactive Context-Aware Network for RGB-T Salient Object Detection](https://mlsanity.com/paper/2211.06097/) (2022)
- [DualGazeNet: A Biologically Inspired Dual-Gaze Query Network for Salient Object Detection](https://mlsanity.com/paper/2511.18865/) (2025)

## Abstract (arXiv)

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 it non-saliency. 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 it image amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to such an image saliency detector. The saliency map is obtained by reconstructing the 2-D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform performs the analysis in the frequency domain. Using available databases, we demonstrate experimentally that the proposed model can predict human fixation data. We also introduce a new image database and use it to show that the saliency detector can highlight both small and large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to predict salient regions on which people focus their attention.