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Image Classification Method Based on Spatial Fisher Vector

A classification method and image technology, applied in the field of image processing, can solve the problems of disorder, without considering the spatial layout information of feature points, ignoring the visual ambiguity of foreground and background, etc.

Inactive Publication Date: 2016-04-13
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

The main shortcomings of this patent are: first, when assigning feature points to each word in the visual dictionary, the hard allocation method of "one feature point corresponds to one word" is used, and one feature point can only be assigned to On one codeword, the "foreground and background visual ambiguity" phenomenon in real images is ignored; second, the frequency histogram constructed during image vectorization is actually an unordered collection of local feature points on the codebook , does not consider the spatial layout information of the feature points at all. For images with obvious hierarchical characteristics, the spatial layout information of the feature points may contain information that is discriminative for classification. Ignoring this information will result in the generated frequency histogram Each frequency component in is completely disordered, and it is impossible to describe the position range of the corresponding object in the image in the image space

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  • Image Classification Method Based on Spatial Fisher Vector
  • Image Classification Method Based on Spatial Fisher Vector
  • Image Classification Method Based on Spatial Fisher Vector

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Embodiment Construction

[0030] The solutions and effects of the present invention will be described in further detail below with reference to the accompanying drawings.

[0031] refer to figure 1 , the implementation steps of the present invention are as follows:

[0032] Step 1: Divide the image set M to be classified into a training set M 1 and the test set M 2 , extract the "scale-invariant feature transformation" feature points of all images in the image set M.

[0033] The implementation of this step can adopt the existing scale-invariant feature transformation method, SURF method and Daisy method. In this example, the scale-invariant feature transformation method is adopted, and the steps are as follows:

[0034] 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:

[0035] D(x,y,σ)=(G(x,y,kσ)-G(x,y,σ))*I(x,y),

[0036] Among them, * represents the convolution operation, I(x, y) represents the image in the image ...

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Abstract

The invention discloses a spatial Fisher vector based image classification method which mainly solves the problems of 'foreground and background visual ambiguities' of images and lack of feature point space distribution information in image description vectors in the prior art. The method includes the implementation 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 feature points by the aid of a Gaussian hybrid clustering algorithm to obtain a code book; (3) utilizing gradient vectors and Cholesky components for generating a Fisher vector of each image; (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 a Fisher vector of each cell block for stitching to generate a space Fisher 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 picture push. Background technique [0002] Image classification is a typical problem in the field of computer vision, and with the massive growth of multimedia data, this problem is particularly prominent. 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 images to achieve image classification. Images are often affected by imaging factors such as viewing angle, illumination, and occlusion, which bring great challenges to image classification. [0003] At present, image classification methods mainly include text label-based image classification methods and content-based image classification methods. [0004] Image classification methods based on text labe...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/66
Inventor 崔江涛汪鹏毕源良崔苗苗王阳
Owner XIDIAN UNIV
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