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Large image classification method based on sparse coding K nearest neighbor histograms

A technology of sparse coding and classification methods, which is applied in the field of massive image classification based on statistical sparse coding K-nearest neighbor histograms, can solve problems such as incompetence for massive image classification tasks, and achieve image classification accuracy, improve discrimination, and improve The effect on image classification accuracy

Inactive Publication Date: 2015-02-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

From this point of view, considering the inherent defects and advantages of the two methods of image space and feature space, it is difficult to use a single method based on image space or feature space for the classification task of massive images.

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  • Large image classification method based on sparse coding K nearest neighbor histograms
  • Large image classification method based on sparse coding K nearest neighbor histograms
  • Large image classification method based on sparse coding K nearest neighbor histograms

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Embodiment Construction

[0018] The specific steps of the massive image classification method based on the sparse coding K nearest neighbor histogram proposed by the present invention are as follows:

[0019] Step 1: Extract N image blocks with a size of s×h from the training image set, s and h are pixel units, and each image block is a D=s×h×d-dimensional vector, when the picture is an RGB image , d=3; when the picture is a grayscale image, d=1; the image block set Patches of the entire training image set is expressed as:

[0020]

[0021] Among them, p i is a column vector composed of pixels of the i-th image block in the image block set Patches, i=1,...,N, N is the total number of image blocks in the image block set Patches, Represents a D-dimensional column vector;

[0022] Step 2: Preprocess the image block set Patches; normalize the image block set Patches to ensure that the dimensions of each data are the same, and each image block p i The normalization formula for is:

[0023] ...

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Abstract

The invention provides a large image classification method based on sparse coding K nearest neighbor histograms, and belongs to the technical field of pattern recognition and information processing. According to the image feature expression provided by the method, histogram statistics is conducted on different scales, and the feature information of all regions of an image is captured to a great extent, so that the obtained image feature has translation invariance, and various deformed images can be distinguished effectively; the accuracy of a large image classification task is improved with image expression as concise as possible, the image expression is extremely concise in the image processing process, the computing complexity is low, and meanwhile, very high robustness is provided for image deformation.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and information processing, relates to massive image processing in computer vision, and in particular to a massive image classification method based on statistical sparse coding K-nearest neighbor histogram. Background technique [0002] In recent years, with the increasing scale of Internet image big data, both the scale of the image database and the diversity of images have reached unprecedented peaks. How to accurately classify massive images has become a research hotspot in related fields. Traditional image classification methods are mainly based on two ideas: 1) image space; 2) feature space. The method based on the image space mainly uses the gray histogram and texture features of the image; the idea of ​​the method based on the feature space is to map the original image to the feature space through a transformation operation such as wavelet transform, and then extract the image...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06V10/50G06F18/2411
Inventor 董乐张宁贺玲
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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