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Image classification method based on MapReduce

A mapreduce framework and classification method technology, applied in the field of image classification, can solve the problem that the MapReduce programming model does not support iterative operations, etc.

Inactive Publication Date: 2015-03-04
LANGCHAO ELECTRONIC INFORMATION IND CO LTD
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AI Technical Summary

Problems solved by technology

Since the Hadoop platform is widely used for big data storage, the MapReduce programming model adopted by Hadoop does not support iterative operations.

Method used

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  • Image classification method based on MapReduce

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Embodiment Construction

[0050] Below according to accompanying drawing of description, in conjunction with specific embodiment, the present invention is further described:

[0051] as attached figure 1As shown, the Hadoop-based computing environment consists of 12 Dell desktops, including one Namenode node and 11 Datanode nodes. The hardware configuration of each node is an i5-2400 3.1 GHz quad-core CPU with 4 GB of memory. The software configuration includes: the operating system is 64-bit CentOS 6.3 (2.6.32-279.e16.x86-64 kernel), the Java environment is Oracle jdk 1.7.0, and Hadoop is 1.04 native version. There is no interface for image input and output on Hadoop, that is: by default, Hadoop does not recognize the image type. In the experiment, by overloading the input and output interface of Hadoop, the customized image reading and writing is realized. The input interface of Hadoop is InputFormat 〈K, V〉, and its sub-interfaces include FileInputFormat 〈K, V〉 and DBInputFormat 〈K, V〉, among whi...

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Abstract

The invention discloses an image classification method based on MapReduce, on Hadoop platform, firstly, using the MapReduce frame for parallel extracting image SIFT characteristic; using the MapReduce frame for sparse coding for extracted SIFT characteristic of each image and obtaining the corresponding sparse vector of the image, generating the sparse characteristic of the image; then, based on the sparse characteristic of the image, using MapReduce frame for training the decision-making tree, generating the random forest aiming at the image characteristic set; using MapReduce combined with the random forest for classified counting each image. Through experimental verification, the image classification method based on MapReduce can obviously raise the classification speed while guaranteeing not lower than single platform classification precision.

Description

technical field [0001] The invention relates to the field of image classification methods, in particular to a MapReduce-based image classification method. Background technique [0002] Image classification refers to the use of computers to automatically analyze images and classify images into one of several categories to replace human visual interpretation. It is the basis for target detection and recognition, image retrieval, information filtering and other application fields. With the advent of the era of big data, images are continuously generated from surveillance cameras, remote sensing satellites, self-media, etc. every day. The architecture based on the storage and processing of big data is completely different from that of traditional file systems, database systems, and parallel processing architectures. Different, it poses a challenge to the classification of image big data. The current mainstream image classification method is mainly divided into two steps: first,...

Claims

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Application Information

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IPC IPC(8): G06K9/66G06K9/46
CPCG06F18/2413
Inventor 黄敏刘晶杨晋博
Owner LANGCHAO ELECTRONIC INFORMATION IND CO LTD
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