Fuzzy correlation synchronized image retrieval method based on color histogram and non-subsampled contourlet transform (NSCT)

A color histogram and synchronous image technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as inability to obtain retrieval results, image retrieval methods that cannot meet user requirements, and failure to obtain descriptions, etc.

Inactive Publication Date: 2015-09-16
SHANXI UNIV
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Problems solved by technology

In addition, people's understanding of images is based on all the features that can be recognized by the human eye. The overall understanding of the image is not based on a certain feature. Therefore, it is often difficult to describe an image from only one aspect. It cannot be fully described, and o

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  • Fuzzy correlation synchronized image retrieval method based on color histogram and non-subsampled contourlet transform (NSCT)
  • Fuzzy correlation synchronized image retrieval method based on color histogram and non-subsampled contourlet transform (NSCT)
  • Fuzzy correlation synchronized image retrieval method based on color histogram and non-subsampled contourlet transform (NSCT)

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Embodiment Construction

[0092] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0093] A fuzzy correlation synchronized image retrieval method based on color histogram and NSCT, such as figure 1 shown, including the following steps:

[0094] (1), perform NSCT texture feature extraction on any image P in the image library and the image Q to be retrieved respectively, the texture feature extraction method of NSCT is as follows:

[0095] The RGB image is converted into a grayscale image, and the grayscale image is decomposed into a three-layer NSCT transformation whose coefficients are {2, 3, 4} and the number of subbands is 4, 8, and 16, and the subband coefficients of 28 subbands are obtained, respectively Calculate the mean μ of the coefficients of each subband i and standard deviation σ i , mean μ i and standard deviation σ i The calculation formula is as follows:

[0096] μ k ...

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Abstract

The invention relates to an image retrieval method. The method comprises the following steps of extracting color characteristics of an image by using a color histogram, and taking two characteristics such as a color vector of the color histogram and the height of a color column as the retrieval basis; calculating similarity by using the fuzzy membership function in a fuzzy set theory, judging the similarity through alpha fuzzy relation; introducing non-subsampled contourlet transform (NSCT) to extract the texture characteristic of the image at the same time; resolving the image by NSCT; extracting the mean value and standard deviation of sub-band coefficient in multiple directions of different layers and taking the mean value and standard deviation as feature vectors and index of the image in an image library, calculating similarity of the images by using the fuzzy membership function in a fuzzy set theory, wherein because the multiscale, multidirectionality and translation invariance property, great direction information can be kept after resolving, the method can completely describe the textural features of the image; finally, the two algorithms are combined, retrieving the image by using comprehensive features.

Description

technical field [0001] The invention relates to an image retrieval method, in particular to an image retrieval method using color features extracted from color histograms and texture features extracted from non-subsampling contourlet transformation to perform integrated features. Background technique [0002] The content expressed by an image is very rich, it contains many aspects of features, and only using one feature cannot describe the entire content of the image. In addition, people's understanding of images is based on all the features that can be recognized by the human eye. The overall understanding of the image is not based on a certain feature. Therefore, it is often difficult to describe an image from only one aspect. Can not get a comprehensive description, and often can not achieve the ideal retrieval effect when the image changes greatly (enlargement, reduction, translation or rotation, etc.). [0003] At present, the single-feature image retrieval method can ...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/5838G06F16/5862
Inventor 张丽红张云霞
Owner SHANXI UNIV
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