Image significance detection method based on region label fusion

A technology of label fusion and detection method, applied in the field of image processing, which can solve the problems of unclear boundaries in image salient areas and not dense internal areas.

Active Publication Date: 2019-03-26
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

[0007] The purpose of the embodiments of the present invention is to provide an image saliency detection method based on region label fusion, which solves the problems of unclear boundaries and non-dense internal regions of image salient regions, and com

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  • Image significance detection method based on region label fusion
  • Image significance detection method based on region label fusion
  • Image significance detection method based on region label fusion

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Embodiment Construction

[0061] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0062] See figure 1 , Provide an image saliency detection method based on region tag fusion, including:

[0063] S1: Use super pixel segmentation algorithm to preprocess the image and over segment the image into several image area blocks;

[0064] S2: Use Gaussian kernel function to obtain area similarity according to the color and location information of the image area block, use the area similarity to perform spectral clustering of the super pixel area, obtain a label set for image segmentation, and save according to the label set Image boundary information;

[0065] S3: Obtain the salient features of the image, and perform the fusion of the salient features under the conditional random field model to obtain a saliency map of roughness;

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Abstract

According to the image significance detection method based on region label fusion, a super-pixel segmentation algorithm is used for preprocessing an image, and the image is segmented into a pluralityof image region blocks; Using a Gaussian kernel function to obtain region similarity, using the region similarity to perform spectral clustering of the superpixel region, obtaining a label set of image segmentation, and storing boundary information of the image according to the label set; Obtaining salient features of the image, and fusing the salient features under a conditional random field model to obtain a roughness saliency map; Utilizing the label set to propagate the boundary information, and comparing and fusing the boundary information and the roughness saliency map to obtain reconstruction of the roughness saliency map; A self-adaptive threshold segmentation mode is adopted to carry out binarization processing on the reconstructed roughness saliency map, a label indication vectoris utilized to label a saliency region into a unified label, isolated points in the saliency region are processed, and more effective saliency region detection is obtained.

Description

Technical field [0001] The invention relates to the technical field of image processing, in particular to an image saliency detection method based on regional label fusion. Background technique [0002] The human visual system can obtain regions of interest based on visual attention mechanisms for different scenes. Each image contains one or more salient targets, and saliency detection is to imitate the visual attention mechanism to obtain important information in the image to improve the efficiency and accuracy of image processing. Image saliency detection has a wide range of applications in image annotation and retrieval, target recognition, image automatic cropping, image compression and other fields, and it is one of the hotspots of computer vision research. [0003] Starting from the visual attention mechanism, saliency detection can be divided into bottom-up detection model and top-down detection model. The bottom-up model mainly uses the underlying features such as color, ...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62G06T7/11
CPCG06T7/11G06T2207/20021G06T2207/20221G06V10/56G06V10/464G06F18/22G06F18/23213G06F18/253
Inventor 郭鹏飞董静
Owner LIAONING TECHNICAL UNIVERSITY
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