Multi-scale segmentation-based saliency detection method

Abstract

The invention relates to a multi-scale segmentation-based saliency detection method. The method includes the following steps that: 1: smoothing image processing is performed on an input image through using bilateral filtering parameters, super-pixel segmentation of different segmentation scales is performed on the processed input image, global smoothness is calculated according to super-pixels obtained through segmentation, the global smoothness and the bilateral filtering parameters are combined to build an adaptive algorithm function adopting a segmentation effect as an objective, bilateral filtering parameters under different scales are solved, and super-pixel points in the optimal smoothed image are obtained; sep 2, initial foreground seeds are obtained through using a target likelihood graph technique, the boundary of the image is adopted as initial background seeds, background seeds and foreground seeds are selected from the initial background seeds and the initial foreground seeds by using a cross-validation method, and a background-based RBB saliency map and a foreground-based RFB saliency map are generated; and step 3, the scale weights of the super-pixels and the seed weights of the background seeds and the foreground seeds are calculated, and the obtained RBB saliency map and RFB saliency map are combined, so that a final saliency map can be obtained.

Description

technical field [0001] The invention relates to the field of image data processing, in particular to a saliency detection method based on multi-scale segmentation. Background technique [0002] In recent years, saliency detection has been a hot topic in the field of computer vision and image processing. At present, many researches in the field of computer vision have used saliency detection as a preprocessing step, such as image compression, image segmentation, object location, image classification and so on. [0003] Generally, the saliency detection methods can be roughly divided into two categories according to the way of information processing, one is the top-down method, and the other is the bottom-up method. The top-down method needs to know the basic attributes of the detected target first, and conduct supervised training, so most of the top-down methods can achieve high accuracy, but these methods often ignore the significance of the salient target. Details. In co...

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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to design a salient object detection method based on multi-scale segmentation that can flexibly select the segmentation scale corresponding to different images, so as to process complex images and extract the salient objects of the image

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