Area-of-interest detection method based on full convolution neural network and low-rank sparse decomposition

Abstract

The invention discloses an area-of-interest detection method based on full convolution neural network and low-rank sparse decomposition. The method comprises steps of 1) carrying out super-pixel clustering on an original image, extracting color, texture and edge characteristics of each super-pixel and forming a characteristic matrix based on the characteristics; 2) in an MSRA database, based on agradient descent method, learning to obtain a characteristic conversion matrix; 3) in the MSRA database, by use of the full convolution neural network, learning to obtain high-level semantic prior knowledge; 4) by use of the characteristic conversion matrix and the high-level semantic prior knowledge, converting the characteristic matrix; and 5) by use of the robust principal component analysis method, carrying out low-rank sparse decomposition on the converted matrix, and according to sparse noise obtained through the decomposition, calculating a saliency map. According to the invention, themethod is used in an image preprocessing process, and can be widely applied in visual working field like visual tracking, image classification, image segmentation and target re-positioning.

Description

technical field [0001] The present invention relates to a method of detecting a region of interest based on a fully convolutional neural network and low-rank sparse decomposition. The method is capable of detecting regions of interest with different background contrasts and background complexity and images of regions of interest with different areas. The detection effect is very good. As an image preprocessing process, the present invention can be widely applied to visual work fields such as visual tracking, image classification, image segmentation, and target relocation. Background technique [0002] With the rapid development and promotion of information technology, image data has become one of the important sources of information for human beings, and the amount of information received by people is increasing exponentially. How to screen out the target area of ​​human interest from massive image information is of great research significance. Studies have found that in co...

AI Technical Summary

Problems solved by technology

This method covered the deficiencies of the literature [13] and [14] to a certain extent, but due to the limitations of the center prior, and the color prior will also fail in complex scenes, the algorithm is more complex for the background. The image detection effect of the

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