Modified Scale-Space Analysis in Frequency Domain Based on Adaptive Multiscale Gaussian Filter for Saliency Detection

Abstract

The salient region is an area in the image that is paid attention from a human. It is a distinctive feature from neighbors such as color, shape or pattern. Saliency detection is a model that imitates the human visual system to perceive the scene. It has been widely used in many vision systems. Many papers use the smoothing or suppressing technique to extract a desirable output as so-called saliency map. Even though most of these researches achieve saliency detection based on the filter, the size of the filter is fixed. This leads to a filter ineffective when applying to the whole area of each image. In order to solve this issue, an adaptive multiscale Gaussian filter (MSS) for scale-space analysis in the frequency domain is proposed. The proposed filter is extended from an adaptive median filter which is a powerful method to remove the noise from the input image. This paper proposes the method that offers the appropriate filter to suppress the repeated pattern spectrum of each region in each image before extracting the saliency map. The experimental result shows that the proposed method outperforms the baseline methods, which includes HFT, Itti, SAL, SR and SUN.