Significance fusion method based on 3D fixation point predication of picture

Abstract

The invention discloses a significance fusion method based on 3D fixation point predication of a picture. The method comprises saliency map generation and picture-based fusion. The saliency map generation comprises acquiring the saliency map of each original picture frame from an original video sequence. The picture-based fusion comprises the steps of constructing an energy function of an originalpicture according to the saliency map through objects of minimizing significance smooth restriction and minimizing significance difference between the original picture and the adjacent original picture; solving the energy function in the original picture, and obtaining a target significance map. According to the significance fusion method, the significance smooth restriction between a super-pixeland an adjacent super-pixel and a significance difference between the original picture and the adjacent original picture are considered so that the significance fusion method realizes relatively highsignificance in predicating different modal characteristics in a multi-modal characteristic fusion process.

Description

technical field [0001] The invention belongs to the fields of image processing and computer vision, and more particularly relates to a saliency fusion method for 3D gaze point prediction based on graphs. Background technique [0002] In the field of visual attention, there are already quite a few models for 2D visual attention, which can be roughly divided into two categories: human gaze point prediction models and salient object detection models. The former computes salient intensity maps at the pixel scale, and the latter aims to detect and segment salient objects or regions in a scene. There are quite a lot of visual attention models for human eye gaze prediction, but the research on gaze prediction models for 3D videos has just begun in recent years. In a nutshell, the framework of most 3D gaze prediction models is extended from 2D gaze prediction models. The framework mainly includes two steps. The first step is to extract a series of feature maps from the original co...

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Problems solved by technology

[0010] In view of the above defects or improvement needs of the prior art, the present invention provides a saliency fusion method based on graph-based 3D gaze point prediction, thereby solving the problem of different modal feature predictions in the process of multi-modal feature fusion in the prior art Significantly inconsistent or even contradictory technical issues

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