A cross-modal image saliency detection method

A detection method and multi-modal image technology, applied in the field of computer vision, can solve the problems of modal noise or error, inability to fuse multi-modal information together, and achieve increased robustness and good multi-modal image saliency. Detection effect, effect of solving heterogeneity problem

Active Publication Date: 2021-09-17
ANHUI UNIVERSITY
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the existing technology is easy to introduce modal noise or error, so that the multi-modal information cannot be well fused together, and a cross-modal image saliency detection method is provided

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  • A cross-modal image saliency detection method
  • A cross-modal image saliency detection method
  • A cross-modal image saliency detection method

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Embodiment Construction

[0042] The embodiments of the present invention are described in detail below. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0043] like figure 1As shown, this embodiment includes the following steps:

[0044] (1) Input paired multi-modal images, and use a superpixel segmentation algorithm to segment different modalities to obtain uniform and similarly sized superpixel regions;

[0045] (2) Design a multimodal image saliency detection model based on the graph manifold sorting algorithm, and introduce cross-modal soft consistency constraints and manifold sorting fit item sparsity constraints;

[0046] (3) Using the superpixels on the four sides of the image as the seed node, calculate the similarity between other nodes and the seed node...

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Abstract

The invention discloses a cross-modal image saliency detection method, which inputs paired multi-modal images, uses a superpixel segmentation algorithm to segment different modalities, and obtains superpixel regions of uniform size and similar size; the design is based on graph flow The multi-modal image saliency detection model of the shape sorting algorithm introduces cross-modal soft consistency constraints and manifold sorting fitting item sparsity constraints; uses the superpixels on the four sides of the image as the seed nodes to calculate the similarity between other nodes and the seed nodes To obtain a preliminary saliency map; refer to the foreground point obtained in the previous stage as a seed node, calculate the similarity of other nodes to this node, and obtain the final saliency map. The present invention proposes a method for complementary fusion of multi-modal images based on graph manifold sorting algorithm, and introduces l 1 The norm implements cross-modal soft consistency constraints and manifold ranking function fitting item sparsity constraints, that is, on the basis of coordinating multiple modalities, partial inconsistencies are allowed to increase the robustness of fitting items.

Description

technical field [0001] The invention relates to a computer vision technology, in particular to a cross-modal image saliency detection method. Background technique [0002] Saliency detection based on manifold sorting is based on background nodes and foreground nodes, and an image saliency detection algorithm is proposed. Firstly, the salient image based on the background node set is calculated by using the background node as prior knowledge, combined with the image color features and positional relationship, because the background node is determined, according to the contrast relationship between the image superpixel and the background node, the foreground object can be superimposed The pixels are well highlighted, but the suppression of background noise is insufficient, and then the foreground is used as prior knowledge, and the saliency image based on the foreground node set calculated by using the image color features and position relationship can suppress the background ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/32G06K9/46
CPCG06V10/255G06V10/56
Inventor 李成龙夏威涂铮铮汤进罗斌
Owner ANHUI UNIVERSITY
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