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Cooperative significance testing method

A detection method and remarkable technology, applied in the field of stereo vision and image processing, can solve the problems of lack of multi-scale inter-graph relationship acquisition method, lack of joint optimization graph method, etc., to achieve good consistency and suppress complex background areas.

Active Publication Date: 2018-04-13
TIANJIN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The methods in the prior art usually lack a multi-scale inter-graph relationship acquisition method; the existing methods often lack the method of jointly optimizing the intra-graph and inter-graph saliency

Method used

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Experimental program
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Embodiment 1

[0039] In order to accurately and completely extract the common saliency objects of the RGBD image group, the embodiment of the present invention designs a collaborative saliency detection method, see figure 1 and figure 2 , the specific implementation steps are as follows:

[0040] 101: Segment the RGB image through the superpixel segmentation method to obtain a uniform and consistent superpixel area, and use the RGBD saliency detection based on the depth confidence measure and multi-cue fusion to fuse the compact saliency and the foreground saliency to obtain Significance value in the graph;

[0041] 102: Based on similarity constraints, saliency consistency constraints, and clustering constraints, express the corresponding relationship between multi-image superpixels as a matching relationship under multiple constraints, and then obtain the matching relationship label between superpixels;

[0042] 103: Fuse the distances calculated by multiple features through an adaptiv...

Embodiment 2

[0047] The scheme in embodiment 1 is further introduced below in conjunction with specific calculation formulas and examples, see the following description for details:

[0048] 201: superpixel segmentation;

[0049] Suppose there are N RGB color images in the image group Its corresponding depth map is Using SLIC (simple linear iterative clustering) superpixel segmentation method to image I iCarry out segmentation, and obtain N after segmentation i A uniform and consistent superpixel region, denoted as Among them, D i is the i-th depth map; is the superpixel region.

[0050] 202: In-graph saliency calculation;

[0051] The intra-graph saliency model is used to calculate the saliency map of a single image in an image group, without involving the relationship between graphs. In a single image, salient objects usually exhibit distinct appearance characteristics from background regions, thereby making salient objects stand out. In addition, depth information, as a sup...

Embodiment 3

[0096] Combine below figure 1 and figure 2 , carry out feasibility verification to the scheme in embodiment 1 and 2, see the following description for details:

[0097] figure 1 The visual detection results of this method are given. The first row is the original RGB color image, the second row is the corresponding depth map, the third row is the ground truth image, and the fourth row is the co-saliency detection result obtained by this method.

[0098] From figure 1 It can be seen from the figure that this method can effectively extract the common saliency target of the image group, that is, the blonde cartoon character, and can effectively suppress the complex background area to obtain a more complete and consistent saliency target.

[0099] Those skilled in the art can understand that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the above-mentioned embodiments of the present invention are for description only,...

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Abstract

A cooperative significance testing method comprises the steps of dividing an RGB picture through a superpixel dividing algorithm, fusing compactness significance and prospect significance, and obtaining an in-picture significance value; based on similarity restriction, significance consistency restriction and clustering restriction, representing correspondence among a plurality of subpixels by a matching relation on the condition of multiple restrictions, and furthermore obtaining matching relation marks among the superpixels; fusing distances which are obtained through calculating a pluralityof characteristics through an adaptive weighting strategy, and obtaining a measure for evaluating similarity between two images; wherein the inter-picture significance value among the superpixels isweighted summation of single-picture significance values of corresponding superpixels in other images, obtaining a weighting coefficient through the similarity measure among the images, and obtainingan inter-picture significance value; performing combined optimization on the in-picture significance value and the inter-picture significance value by means of intersected label propagation; and performing weighted fusion on the initial in-picture significance value, the inter-picture significance value, the optimized in-picture significance value and the optimized inter-picture significance valuefor obtaining a final cooperative significance result.

Description

technical field [0001] The invention relates to the technical fields of image processing and stereo vision, in particular to a collaborative saliency detection method. Background technique [0002] As a cutting-edge technology in the field of artificial intelligence and computer vision, visual saliency detection technology has been widely used in many visual tasks such as image retrieval, compression, perceptual enhancement, and image redirection. With the advent of the big data era, collaborative saliency detection technology is in the ascendant, and its purpose is to simultaneously detect common salient objects in multiple images. [0003] Different from traditional single-image saliency detection models, co-saliency detection models aim to discover common salient objects from image groups containing two or more related images, and the categories, intrinsic features, and locations of these objects are often different. is unknown. Therefore, the co-saliency target needs t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46
CPCG06V10/443G06V10/56G06V10/462
Inventor 雷建军丛润民侯春萍张静范晓婷彭勃
Owner TIANJIN UNIV
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