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Method for recognizing natural images and computer generated images based on multi-wavelet transform

A technology of multi-wavelet transform and image generation, which is used in computer parts, character and pattern recognition, calculation, etc.

Inactive Publication Date: 2011-05-18
NINGBO UNIV
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  • Description
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Problems solved by technology

At present, the multiwavelet transform has been well applied in image denoising, image processing and image coding, but there are no relevant research reports on the application of multiwavelet transform to computer-generated image detection at home and abroad.

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  • Method for recognizing natural images and computer generated images based on multi-wavelet transform
  • Method for recognizing natural images and computer generated images based on multi-wavelet transform
  • Method for recognizing natural images and computer generated images based on multi-wavelet transform

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Embodiment Construction

[0072] The present invention will be further described in detail below with reference to the embodiments of the accompanying drawings.

[0073] The present invention is based on a multi-wavelet transform natural image and a computer-generated image recognition method, comprising the following steps:

[0074] (1) Input M training sample images and N test sample images, convert the training sample images and test sample images to HSV color space, and obtain the corresponding hue H component image, corresponding saturation S component image and corresponding brightness respectively V component image, and identify the category of the training sample image;

[0075] At present, most computer-generated image detections for JPEG are based on RGB color space, but from the perspective of human color perception, RGB color space cannot describe color characteristics well, while HSV space is more in line with human perception. Color recognition, this invention chooses to extract features...

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Abstract

The invention discloses a method for recognizing natural images and computer generated images based on multi-wavelet transform, comprising the following steps of: (1) transforming training sample images and test sample images to an HSV color space, respectively obtaining corresponding hue component images, saturation images and luminance images; (2) pre-processing each component image and then performing first-order multi-wavelet transform to each component image; (3) taking 16 sub-bands of the obtained hue component images, saturation images and luminance images as objects, and calculating the mean value, the variance, the skewness and the kurtosis of each sub-band wavelet coefficient, thereby obtaining 192 characteristic values; and (4) calibrating and normalizing the characteristic values, and then substituting to an SVM classifier for training and testing, thereby obtaining classes of the images. The method is high in detection recognition rate and low in calculation complexity.

Description

technical field [0001] The invention relates to a method for blind forensics of digital images, in particular to a method for recognizing images generated by computers and natural images based on multi-wavelet transform. Background technique [0002] As an effective carrier of information transmission, image data has become the most important way to obtain and publish information in our work and life due to its intuitive, easy-to-understand and very convincing features. However, with the development of information technology, various image processing and generation software continue to emerge, and various production techniques are continuously improved and improved. 3D image production software such as 3Dmax, Maya, Softimage can generate almost perfect images, which can be combined with natural The images are comparable, and the naked eye is almost indistinguishable from these images. These technologies are like a double-edged sword. On the one hand, these images are used i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/54
Inventor 王让定郭克张荣严迪群徐达文
Owner NINGBO UNIV
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