Image Saliency Detection Method Based on L1 Regularization

Abstract

The present invention discloses an image significance detection method based on L1 regularization. The method includes the following steps: performing Fourier transform on an image to be detected, performing different scales of Gaussian filter on a frequency domain amplitude spectrum thereof, and constructing a scale space of the frequency domain; designing a sparse optimizing problem based on total variation, and using a SplitBregman method to solve the problem to obtain a group of candidate significance images; and using two-dimension entropy of an image as a selection standard, selecting an image with the smallest two-dimension entropy from the candidate significance images and performing empty domain Gaussian filter, to obtain a final significance image. The present invention combines the characteristic of the empty domain and the frequency domain of the significance image, effectively eliminates the influence caused by complex backgrounds, and can solve with high efficiency. Compared with the previous significance detection method for frequency domain analysis, the present invention obtains better effects on both detection of point of fixation of human eyes and detection of article division.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image saliency detection method based on L1 regularization. Background technique [0002] Image-based object detection is an important topic in the field of computer vision and pattern recognition, and has a wide range of applications in the fields of image segmentation, image retrieval and robot autonomous perception. Among various object detection methods, saliency methods have attracted the attention of a large number of researchers for their exploration and simulation of human visual attention mechanism. The human visual mechanism can quickly retrieve objects of interest from a large amount of observed scene information, thus greatly improving the efficiency of human understanding of the scene and the speed of response. The saliency detection method is based on this principle. By analyzing the image content, the part of the image that is significantly...

AI Technical Summary

Problems solved by technology

However, since the spatial information is not considered, when the image scene structure is complex and there are many high-frequency detail components, the traditional frequency domain analysis method will be greatly disturbed by the complex background, resulting in many messy noise points. Causes false detection of significant areas, on the other hand, it will also affect the accuracy of scale selection based on two-dimensional entropy

Images