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Saliency detection method based on conditional random field

A conditional random field and detection method technology, applied in the field of image processing and image target detection, can solve the problems of high computational complexity, low resolution, poor definition of target boundary, etc., achieve small computational complexity, high resolution of results, Define precise effects

Inactive Publication Date: 2014-10-01
HOHAI UNIV
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Problems solved by technology

[0005] The main disadvantages of early image saliency algorithms are low resolution, poorly defined object boundaries, and high computational complexity.

Method used

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  • Saliency detection method based on conditional random field
  • Saliency detection method based on conditional random field
  • Saliency detection method based on conditional random field

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Embodiment Construction

[0038] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0039] like figure 1 As shown, a kind of saliency detection method based on conditional random field provided by the present invention comprises the following steps:

[0040] Step 10: collect image data; define the collected image as I.

[0041] Step 20, performing salient feature extraction on the image obtained in step 10 using three different methods to obtain a salient feature map corresponding to three different salient feature functions;

[0042] In this embodiment, the multi-scale comparison method, the histogram method around the center and the color space distribution method are used for feature extraction.

[0043] 1. Multi-scale comparison method

[0044] In saliency detection, contrast methods are most commonly used on local features. Without knowing the size of salient objects, we employ a multi-scale approach for saliency detection in local regions se...

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Abstract

The invention discloses a saliency detection method based on a conditional random field. Saliency detection is considered to be an image annotation problem. Multi-scale comparison is used, and salient feature graphs are obtained through three different saliency calculation modes including a center-periphery histogram and color and space distribution. The weight of saliency of each salient feature graph is calculated through CRF study, and a model parameter is obtained through a maximum likelihood estimation method to estimate the optimal solution. Finally, the CRF is used for detecting and testing images. The method can detect salient objects more precisely, results obtained through detection are high in resolution ratio, an object boarder is defined precisely, and the method is little in calculation complexity.

Description

technical field [0001] The invention belongs to the field of image processing and image target detection, in particular to a saliency detection method based on a conditional random field. Background technique [0002] Vision is the most important perception of human beings. More than 90% of the external information that the human brain can receive comes from the visual perception of the human eye. The main function of vision is to explain the surrounding environment of people's life and interact with it. The rapid development of information technology has led to the expansion of various image information. People have to use computer systems to process and analyze these massive data. But it is worth noting that: on the one hand, the increase speed of image data is much faster than the improvement of computer processing power; on the other hand, the content that people care about is usually only a small part of the entire data set. For this reason, it is unrealistic and unnec...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/66
Inventor 王敏范佳佳
Owner HOHAI UNIV
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