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Image scene classification method based on multi-characteristic fusion

A multi-feature fusion and scene classification technology, applied in the field of computer image processing, can solve problems such as misclassification and misclassification of image scene classification methods, and achieve the effects of high efficiency, shortening training time, and improving classification accuracy.

Inactive Publication Date: 2017-05-31
GUANGXI NORMAL UNIV
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is the problem of misclassification and misclassification in existing image scene classification methods, and provides an image scene classification method based on multi-feature fusion, which can ensure classification efficiency while improving classification accuracy

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  • Image scene classification method based on multi-characteristic fusion

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Embodiment Construction

[0027] For different objects in the image, the advantages of various features are different, and there are complementary phenomena among them. Multi-feature fusion can solve the deficiency of single feature description. The present invention proposes an image scene classification method based on multi-feature fusion. First, the GIST feature, SIFT feature and PHOG feature of the image are extracted. Since GIST features belong to sparse grid division, different scene features may be included in a grid, and the specific details in it may be ignored. The SIFT feature is a local feature widely used in image scene classification to achieve precise positioning of feature points. The PHOG feature is a spatial shape description, which characterizes the local shape of an image and the spatial relationship of its shape. The combination of the three to describe the image scene can provide richer information, and the features can complement each other. Then, Locality-constrained linear c...

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Abstract

The invention discloses an image scene classification method based on multi-characteristic fusion; the method comprises the following steps: firstly extracting image GIST characteristics, SIFT characteristics and PHOG characteristics; carrying out local constraint linearity coding for image SIFT characteristics, and pooling SIFT characteristic sparse coding so as to obtain a sample image SIFT characteristic sparse vector; cascading the sample image GIST characteristics, SIFT characteristics and PHOG characteristics so as to form the sample image final characteristic expression, and inputting the sample image final characteristic expression into a linear classifier for training; using said method to extract image expressions from to-be-classified images, and inputting the image expression into a trained linear SVM classifier so as to finish the classification work. The method can improve the classification precision, and can enhance the system robustness.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to an image scene classification method based on multi-feature fusion. Background technique [0002] Image understanding (IU) is the semantic understanding of images. It takes the image as the object, knowledge as the core, and studies what objects are in the image, the relationship between the objects, what scene the image is, and how to apply the scene. Among them, image scene classification is a branch of image understanding. The so-called image scene classification is to automatically determine which scene class (such as beach, forest and street) an image in a set of semantic categories belongs to. Image scene classification technology has always attracted people's research attention. It has been applied in many ways. Although people have made great progress in extracting features, image scene classification is still a challenging task due to the influence ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/443G06V10/50G06V10/513G06V10/462G06F18/2411
Inventor 李志欣李艳红张灿龙
Owner GUANGXI NORMAL UNIV
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