Amplitude spectrum analysis based salient object detection method

Abstract

The invention discloses an amplitude spectrum analysis based salient object detection method, which comprises the steps of extracting a brightness feature I, an antagonistic feature RG and an antagonistic feature BY from an acquired image; the extracted features are transformed into a frequency domain through quaternion Fourier transform so as to acquire an amplitude spectrum, a phase spectrum and an intrinsic axis spectrum of the image; detecting the size of each salient object and a central position of each salient object in the image by using an image signature operator; acquiring an optimal filtering scale corresponding to each of the different salient object through utilizing a specific relation between the optimal filtering scale of the amplitude spectrum and the size of the salient objects, and respectively carrying out different scales of Gaussian filtering on the amplitude spectrum of the image; determining a weight value of an optimal saliency map corresponding to each salient object according to central bias Gaussian distribution and salient object locations, carrying out adaptive Gaussian weight fusion on the acquired different saliency maps, and calculating a fused saliency map; carrying out Gaussian filtering on the fused saliency map; and performing normalization on saliency values to acquire a final saliency map. The method disclosed by the invention can suppress the background quickly and effectively, the salient objects are highlighted uniformly, and more salient information of the image is reserved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for detecting a salient target based on amplitude spectrum analysis. Background technique [0002] With the development of computer science and technology, the computational model of visual attention has gradually become a hotspot of interest to researchers in computer vision and image processing, and has been increasingly applied to the field of computer vision, such as image segmentation, object recognition, image redirection and video compression etc. [0003] The study of visual attention in computing is known as saliency detection. Currently, research on saliency detection can be roughly divided into two categories: gaze focus prediction and salient object detection. Gaze focus prediction aims to model human eye viewpoints by computing saliency maps. Inspired by the feature fusion theory and neurobiological framework proposed by Koch and Ullm...

AI Technical Summary

Problems solved by technology

The above methods have achieved certain results, but they can only detect small areas with edges and complex textures, and the detection effect for areas with larger targets is not ideal.

Recently, Li et al. proposed a saliency detection algorithm based on spectral scale space (Li J, Levine M D, An X, et al. Visual saliency based on scale-space analysis in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 ,35:996–1010.) solved this problem to a certain extent, but due to the method of using the entropy value to select a single saliency map, on the one hand, the salient regions are highlighted unevenly, on the other hand, a lot of saliency information will be lost

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