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A Pest Image Recognition Method Based on Multi-feature Sparse Representation Technology

A sparse representation, image recognition technology, applied in the field of image recognition, can solve problems such as poor recognition ability, and achieve the effect of improving accuracy and effective fusion

Active Publication Date: 2017-09-22
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the defect that the pest image recognition method in the prior art has poor recognition ability in a complex real environment, and to provide a pest image recognition method based on multi-feature sparse representation technology to solve the above problems

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Embodiment Construction

[0052] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0053] Such as figure 1 As shown, a kind of pest image recognition method based on multi-feature sparse representation technology of the present invention is characterized in that, comprises the following steps:

[0054] The first step is the multi-feature extraction of the pest image, extracting the color features, texture features and shape features of the pest image. The underlying features of pest images include color features, texture features, and shape features. The current pest recognition is mainly based on a single underlying feature of the image, and lacks the comprehensive application of the underlying feature. A single bottom-level feature limits the possibility of combining many features to interpret and understand the...

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Abstract

The invention relates to a pest image identification method based on multi-feature sparse representation technology, which solves the defect of poor identification ability of the pest image identification method in a complex real environment compared with the prior art. The invention includes the following steps: multi-feature extraction of pest images; construction of multi-feature training sample matrix; multi-feature fusion learning; multi-feature fusion identification. The invention improves the accuracy of pest identification. The corresponding feature training sample matrix is ​​constructed by using the color feature, shape feature and texture feature of the pest image. Combined with the sparse representation recognition framework, the effective fusion of different features is achieved by fusing the recognition results under the three features of color, shape and texture.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a pest image recognition method based on multi-feature sparse representation technology. Background technique [0002] The rapid development of computer image processing technology and theory provides advanced technical means for the identification of pests. Due to its advantages of fast speed, high accuracy, and large amount of information, it has been widely used in pest identification in recent years. Using this technology can timely and accurately identify pests, reduce the use of pesticides, improve crop yield and quality, and protect the ecological environment. At this stage, researchers have proposed a variety of pest image recognition methods, which have excellent performance under the premise that the environment is effectively controlled. However, complex farmland backgrounds, different postures of pests, and different appearances in real scenes vary greatly...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/66G06K9/00
Inventor 王儒敬张洁谢成军李瑞洪沛霖宋良图董伟周林立郭书普张立平黄河聂余满
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI