Spatial local clustering description vector based image classification method

A technology of local aggregation and vector description, applied in the field of image processing, it can solve the problems of disorder of each frequency component, not considering the spatial distribution of feature points, not considering the spatial structure and layout information of feature points, etc., to achieve the effect of improving accuracy.

Inactive Publication Date: 2013-09-11
XIDIAN UNIV
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Problems solved by technology

The main disadvantage of this patent is that the constructed frequency histogram is actually an unordered set of local feature vectors. Using this unordered set to vectorize an image does not consider the spatial structure and layout information of feature points at all. , for images with obvious hierarchical characteristics, the spatial layout information of feature points may contain information that is decisive for classification, ignoring this information severely limits the ability of the frequency histogram to describe the image
[0006] Another image classification method widely used in theoretical r

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  • Spatial local clustering description vector based image classification method
  • Spatial local clustering description vector based image classification method
  • Spatial local clustering description vector based image classification method

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[0026] The solutions and effects of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0027] refer to figure 1 , the implementation steps of the present invention are as follows:

[0028] Step 1, divide the image set M to be classified into a training set M 1 and the test set M 2 , to extract the "scale-invariant feature transformation" feature points of all images in the image set M.

[0029] The implementation of this step can use the existing scale-invariant feature conversion method, SURF method and Daisy method. In this example, the scale-invariant feature conversion method is used. The steps are as follows:

[0030] 1a) Use the Gaussian convolution kernel to generate the Gaussian difference scale space D(x,y,σ) of an image in the image set M:

[0031] D(x,y,σ)=(G(x,y,kσ)-G(x,y,σ))*I(x,y),

[0032] Among them, * represents the convolution operation, I(x,y) represents the image in the image set M, σ repres...

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Abstract

The invention discloses a spatial local clustering description vector based image classification method which mainly solves the problem of lack of feature point space distribution information in image description vectors in the prior art. The method includes the steps: (1) extracting 'scale-invariant feature transformation' feature points of all images; (2) in a feature point space of images in a training set, clustering the obtained feature points by the aid of a mean clustering algorithm to obtain a code book; (3) utilizing difference vectors for generating a local clustering description vector of each image in an image set; (4) performing 2X2 space region division on each image, and making statistics on number of feature points and coordinates of each cell block; (5) utilizing the local clustering description vector of each cell block for stitching to generate a spatial local clustering description vector of each image; and (6) utilizing a support vector mechanism for constructing a classification hyperplane to achieve image classification. The method has the advantages that image information can be described accurately, accuracy rate of image classification is improved, and the method can be used for large-scale image classification and retrieval system construction.

Description

technical field [0001] The invention relates to the technical field of image processing, and relates to an image classification method, which can be used for intelligent image classification management and web image push. Background technique [0002] Image classification is a typical problem in the field of computer vision, and it is particularly prominent with the massive growth of multimedia data. Image classification is usually based on the semantic content of the image, such as specific scenes, specific inclusions, etc., to add different category labels to the image to achieve image classification. Since images are often affected by imaging factors such as viewing angle, illumination, and occlusion, it brings great challenges to image classification. [0003] At present, image classification methods mainly include image classification methods based on text labels and image classification methods based on content. [0004] Image classification methods based on text lab...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 崔江涛汪鹏毕源良崔苗苗王阳
Owner XIDIAN UNIV
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