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Big data image classification method

A classification method and big data technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of manual labeling samples, increased development costs of image classification software, and high cost of use

Active Publication Date: 2014-01-01
SOUTH CHINA UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, big data image classification faces difficulties such as the huge number of categories and the huge number of samples that need to be classified.
Linear discriminant analysis is relatively expensive for big data. In order to obtain a certain classification performance, it requires a large number of manually labeled samples.
This greatly increases the development cost of image classification software and requires a large number of manually labeled samples

Method used

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Embodiment

[0047] In order to clearly illustrate the effectiveness of the present invention for image classification, such as figure 1 As shown, in this embodiment, a handwritten digital image classification test is carried out, and compared with the classic linear discriminant analysis (LDA). The test data selects the common USPS data set, the data is from 0 to 9, a total of 10 categories, 9298 samples, the specific implementation steps are as follows: (combining the embodiment and figure 1 Combined to specifically describe the test steps and list the test results):

[0048] Step 1: Collect 10 image samples for each category, a total of 100 samples as the training set X, ie X=[x 1 ,x 2 ,...,x N ]∈R D×N , the sample dimension is D=256, and each sample has a corresponding category mark C i ∈ Z n . The remaining samples are used as the test data set Xu.

[0049] 2) Establish a local optimization objective function:

[0050] For each labeled sample x i , we can find the in-class s...

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Abstract

The invention discloses a big data image classification method. The big data image classification method comprises a first step of enabling image samples to be collected to serve as a training set, a second step of searching a projection matrix optimal in big data image classification, a third step of performing projection on data without marks and a fourth step of adopting a minimum distance classifier to classify the samples after projection. According to the method, local geometric information of sample distribution can be effectively utilized, classified discrimination information is extracted, dependence of big data image classification on manually marked samples is reduced, storage cost in the training process is effectively reduced, and the big data image classification method has higher classification accuracy than a representative image classification method based on linear discrimination analysis.

Description

technical field [0001] The invention relates to an image classification technology in the technical field of pattern recognition and artificial intelligence, in particular to a big data image classification method, which is a supervised learning image classification method. Background technique [0002] With the rapid development of the mobile Internet, more and more smart phones and tablet computers with digital cameras have entered people's lives, and it is easy to generate a large number of personal digital images. Although it is a common method to manage images by using time and directory, it lacks the effective management of images at the semantic level. Therefore, the supervised learning method is used to obtain the image classification model by learning the manually labeled data, and then automatically classify the unlabeled images. Since images usually have very high feature dimensions, dimensionality reduction methods can help improve recognition performance. [0...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/583G06F18/24
Inventor 金连文陶大鹏王永飞
Owner SOUTH CHINA UNIV OF TECH
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