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Method for extracting significant object based on region significance

An object extraction and saliency technology, applied in image data processing, instrument, character and pattern recognition, etc., can solve the problem of image scale-invariant saliency, inability to recognize salient image features, loss, etc.

Active Publication Date: 2009-09-02
SHANGHAI MUNICIPAL ELECTRIC POWER CO +1
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AI Technical Summary

Problems solved by technology

Furthermore, existing image saliency detection methods cannot identify salient image features that appear at different image scales
If the saliency problem of invariant image scale is not solved, the salient features on some image scales will be lost, resulting in unsatisfactory image segmentation results.

Method used

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  • Method for extracting significant object based on region significance
  • Method for extracting significant object based on region significance
  • Method for extracting significant object based on region significance

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Embodiment Construction

[0062] Embodiments of the salient object extraction method based on region saliency of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0063] figure 1 It is a flowchart of the present invention. At first, a scale-invariant saliency image is obtained by establishing a Gaussian image pyramid and a contrast image pyramid of an input image, and the input image is divided into different regions with a non-parametric kernel density estimation method; The ratio of the regional saliency of each regional combination and its complement; finally, the salient objects are extracted by taking the maximum value of this ratio. The specific implementation process is as follows:

[0064] 1. Input an image and build a scale-invariant saliency image:

[0065] ①, Convert the input image to L * a * b color space;

[0066] ②, use the formula (1) to establish a Gaussian image pyramid;

[0067] ③, using the formulas (2) and (3) to...

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Abstract

The invention discloses a method for extracting a significant object based on region significance. The method comprises the following steps: firstly, establishing a significant image with constant scale through by calculating the multiresolution contrast feature of an input image, and dividing the input image into different regions by a non-parameter nuclear density evaluation method; secondly, and then, calculating specific values of region significance of each region assembly and a complementary set thereof; and finally, extracting the significant object through by acquiring the maximum value in the specific values, which comprises the following steps: (1) inputting the an image, and establishing the a significant image with constant scale; (2) inputting the image to realize image segmentation; and (3) extracting the significant image. The method is combined with the region significance, not only can accurately extract a single significant object, but also can extract a plurality of significant objects, so that the extracted significant object can meet the requirement of human vision, and can improve the accuracy of segmentation.

Description

technical field [0001] The invention relates to a computer image processing method, in particular to an image segmentation method. Background technique [0002] Image segmentation is an important issue in the fields of image analysis, pattern recognition and computer vision, and it is also a difficult issue. The ultimate goal of image segmentation is to segment objects with specific practical significance, that is, semantic objects. Some methods use recognizable high-level information (such as faces and text) and image saliency to locate salient objects in images. However, its use is limited because some images do not have identifiable high-level information, or even if identifiable high-level information exists, it is difficult to automatically extract it. Image saliency is always available, but it does not provide enough information for locating salient objects in an image. Low-level spatial features do not necessarily match salient objects well. For example, some high-...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/46
Inventor 韩忠民刘志颜红波李伟伟张兆杨
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO
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