Method for detecting synergy significance of RGBD images based on multi-core enhancement and significant fusion

Abstract

The present invention discloses a method for detecting synergy significance of an RGBD images based on multi-core enhancement and significant fusion. The method comprises: (1) dividing a group of color images with a common significant object into several areas and calculating a significance graph of a single RGBD image; (2) carrying out sample selection, and selecting a category with the highest synergy significance value in the optimal clustering results, taking areas in the category as the positive sample, and taking the areas with the significance value of a single image less than the threshold as the negative sample; (3) using the image random sampling method to generate different training sets to learn a plurality of different models, so as to obtain the synergy significance graphs based on the multi-core enhancement; and (4) carrying out linear fusion on the synergy significance graph based on the multi-core enhancement obtained in the step (3) and the basic significance graphs obtained in the step (2) to obtain fusion synergy significance graphs, evaluating the quality of each fusion synergy significance graph, and taking the quality evaluation as the weight to carry out adaptive fusion so as to obtain final synergy significance graphs of the RGBD images.

Description

technical field [0001] The present invention relates to an image collaborative saliency detection method, in particular to an RGBD image (RGB color image and its corresponding depth image) collaborative saliency detection method based on multi-core enhancement and saliency fusion, aiming to obtain from a group of images with common saliency Co-salient objects are detected in RGBD image sets of objects. Background technique [0002] With the vigorous development of the Internet and multimedia technology, people can obtain image information from more and more fields such as surveillance video, social network, news reports, etc. Therefore, how to capture the key information in the image is the focus of people's attention today. Saliency detection is a challenging field that aims to extract regions of the main objects of interest in images or videos, while co-salient object detection, an emerging branch of visual saliency, aims to collect Extract common salient objects. Co-sa...

AI Technical Summary

Problems solved by technology

In 2015, Fu et al. proposed an object-based collaborative segmentation algorithm that uses depth information, which can effectively deal with noise images that have no common objects or appear more than once. Using several existing RGB saliency detection models with better performance, collaborative saliency The saliency model and the RGBD saliency model are combined according to the rank constraints to obtain the final RGBD co-saliency map, but the correlation of depth information between different RGBD images is ignored.

But in terms of RGBD collaborative saliency detection, there is still a lot of room for development

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