Image salient region detection method based on joint sparse multi-scale fusion

Abstract

The invention belongs to the technical field of image salient region detection and particularly discloses an image salient region detection method based on joint sparse multi-scale fusion. The image salient region detection method comprises the following steps: (1) constructing a multilayer Gaussian pyramid for a training image to realize multi scales and training to obtain a dictionary under each scale; (2) obtaining an image block of each pixel point in a test image and carrying out joint sparse solution of a sparse representation coefficient of the image block under each scale; (3) taking the sparse representation coefficient as a feature to carry out saliency calculation; (4) fusing salient results under the multi sales to obtain a final salient image. The image salient region detection method has the benefits that the purpose of extracting a region capable of catching people's eyes in any given image is realized; the image salient region detection method has the advantages that firstly, the effect under different image scales is overcome under multi-scale operation; secondly, a joint sparse framework is very beneficial to saliency calculation; experiments show that the results obtained by the method have better robustness and are inferior to those obtained according to most of the conventional methods.

Description

technical field [0001] The invention belongs to the technical field of image salient area detection, and can be used to extract areas of interest to human eyes in any given image, and provide better results for image processing subsequent video image compression, image segmentation, target recognition, image repair, image retrieval, etc. The reference information of , specifically an image salient region detection method based on joint sparse multi-scale fusion. Background technique [0002] 80% of the information that human beings obtain from the external environment comes from the visual system. When facing a complex scene, the human eyes will quickly shift their gaze to the areas of interest, and give priority to these areas. For further processing, this special processing mechanism of the human eye is called the visual attention mechanism. In daily life, the human eye obtains a large amount of information every day, and processes and processes it automatically and effic...

AI Technical Summary

Problems solved by technology

[0004] Although the current bottom-up salient region detection algorithm has achieved good results, most methods, such as the classic Itti method and SR method, have a very serious problem and defect in their calculation process

When detecting, it is easy to make the detection results concentrate on the edge of the target, and it is difficult to detect the entire salient target, because most bottom-up methods use the center-peripheral difference operation, because the pixels on the edge and the surrounding pixels The feature difference of the point is large, and the attention degree is high; while the pixel point located in the center area of ​​the target has a small difference with the surrounding pixel point feature, so the attention degree is low

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