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Mass image classification method based on distributed K-means

A classification method, distributed technology, applied in character and pattern recognition, instrumentation, computing, etc.

Active Publication Date: 2015-09-23
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0003] The present invention will solve the problem of feature extraction of large-scale images, so as to achieve the purpose of image classification, aiming at the accuracy of image classification, a method based on distributed K-means is proposed The massive image classification method, the research is based on the big data processing platform Hadoop, a parallel image feature extraction algorithm is proposed, the multi-classification problem of the image, and the final image classification is completed by using the DAG-SVM classifier

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  • Mass image classification method based on distributed K-means
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[0036] The test experiment hardware and software environment of this embodiment is as follows, and its experimental topology picture Such as picture 5 shows:

[0037] Hardware environment:

[0038] Computer type: desktop;

[0039] CPU: Pentium(R) Dual-Core CPU E5600 2.93GHz

[0040] Memory: 4.00GB (3.49GB available)

[0041] System type: 32-bit operating system

[0042] Graphics card: integrated graphics

[0043] Software Environment:

[0044] IDE: Eclipse

[0045] picture Like Processing SDK: JavaCV

[0046] Development language: Java;

[0047] Such as picture 1 The present invention is aimed at large-scale picture A feature extraction algorithm for classification, including the following steps:

[0048] Step 1. Training picture like preprocessing;

[0049] input training picture Like a data set, and each training picture image divided into multiple picture like blocks, for each picture Regularization and whitening operations are performed sequentially on th...

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Abstract

The invention provides a mass image classification method based on distributed K-means, and belongs to the technical field of machine learning and image processing. The mass image classification method based on distributed K-means can be applied to large-scale image classification, adopts the distributed K-means algorithm to extract image characteristics on a big data processing platform Hadoop, and finally achieves the purpose of classifying large-scale images. According to the invention, through the design of performing dictionary learning on large-scale image data, and constructing a characteristic mapping function and a classification algorithm, a characteristic extracting algorithm based on the distributed K-means is provided on the basis of the big data processing platform Hadoop. The method avoids the tedious work of manually designing large-scale image characteristics, and reduces training time under the premise of ensuring classification accuracy; and the achievement of the invention has significant meanings in aspects of large-scale database management, military and medical treatment.

Description

technical field [0001] The invention belongs to the technical field of machine learning and image processing, and relates to massive image processing on a distributed platform, in particular to a massive image classification method based on distributed K-means. Background technique [0002] In recent years, clustering algorithms have been widely used in daily life. In business, clustering algorithms help analysts extract specific consumption information from various consumption databases, and summarize the consumption patterns reflected in the consumption information. Clustering algorithm is an important part in the field of data mining. It can usually be used as a good tool to discover the deep-level feature expression in the database. At the same time, it can summarize the characteristics of each specific category. Most importantly, clustering Algorithms can be used as preprocessing steps of various algorithms in the field of data mining. With the continuous increase of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 董乐张宁
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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