Method and system for extracting significant target region of image based on iterative sparse representation

Abstract

The invention provides a method and system for extracting a significant target region of an image based on iterative sparse representation. Firstly, superpixel segmentation is performed on an originalimage by using an SLIC segmentation method with a plurality of groups of different numbers of pixel element parameters to generate a group of split images with different sizes of superpixel regions;for segmentation results of each scale, taking classical visual attention detection results as the initial saliency map constrained foreground and foreground sample region selection, calculating reconstruction residual of each superpixel region as a significant factor by the sparse representation process, optimizing significant test results under the single dimension in combination with recursiveiterative operations, and finally obtaining final significant targets and test results through multi-scale saliency map fusion. According to the method and system for extracting the significant targetregion of the image based on iterative sparse representation, the shortcomings of inconsistency in the single-target saliency evaluation, the difficulty in detection of an image edge significant target, and the incomplete extraction of multiple significant targets in a conventional method are effectively overcome.

Description

technical field [0001] The invention belongs to the field of computer vision and image processing, and relates to an image salient target area extraction technology based on iterative sparse representation. Background technique [0002] Image visual saliency analysis is a basic research project that computer vision, psychology, neuroscience and other fields attach great importance to. reflect. Through the image saliency analysis, the target area that people are interested in can be effectively extracted, and the data compression can be successfully realized, and the efficient management and utilization of the data can be completed, which is also the basic link of many image processing problems. [0003] Since the first automatic saliency analysis of images was realized by computer in 1998, as its application prospects have been continuously explored, novel automatic detection algorithms for salient objects have emerged in an endless stream. From the perspective of solution...

AI Technical Summary

Problems solved by technology

With the deepening and expansion of research and application, the dependence of the aforementioned methods on hypothetical conditions has become increasingly prominent, as follows: 1) when the salient target is close to the edge of the image, it is usually impossible to achieve correct detection; 2) based on local The method of contrast analysis, the extracted salient target area is incomplete, and the internal saliency evaluation of the target is not uniform; 3) The method based on global contrast analysis often misses detection when dealing with the problem of multiple salient targets at the same time

Therefore, how to overcome the shortcomings of traditional methods, weaken the dependence of hypothetical conditional constraints in the absence of high-level cognitive information, improve the unity and completeness of salient target extraction, and strengthen the adaptability of algorithms still need further research and overcome. technical problem

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