Image classification method based on topic model with monitoring shared assembly

A topic model and classification method technology, applied in the field of image processing, can solve the problems of ignoring topic relevance and increasing the time complexity of the classification method, so as to overcome the poor representation effect, improve the classification accuracy rate, and achieve the effect of good representation effect.

Active Publication Date: 2014-05-21
XIDIAN UNIV
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Problems solved by technology

Although the method disclosed in this patent application increases the number of dictionaries, improves the ability of the dictionary to represent images, and improves the accuracy o

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  • Image classification method based on topic model with monitoring shared assembly
  • Image classification method based on topic model with monitoring shared assembly
  • Image classification method based on topic model with monitoring shared assembly

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[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] combined with figure 1 , the specific steps to realize the present invention are described as follows:

[0040] Step 1. Establish a training set of natural images and a test set of natural images.

[0041] The present invention adopts an international standard natural image category library, which includes 13 image categories, randomly selects 100 images from each image category, and forms the selected images into a natural image training set. The remaining images after selecting the natural image training set from the international standard natural image category library are used to form the natural image test set. In the embodiment of the present invention, the images of each category in the international standard natural image category library are as follows: figure 2 shown. figure 2 middle figure 2 (a) is an image of a suburban villa, figure 2 (b) ...

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Abstract

The invention discloses an image classification method based on a topic model with a monitoring shared assembly. The method mainly solves the problems that in the prior art, the number of parameters is large, correlation of topics is ignored and representation effects of potential semantic features on images are poor. The method includes the implementation steps of establishing a natural image training set and a natural image test set, generating a vision dictionary, generating image sparse representation vectors, generating topic distribution vectors, establishing a natural image classification model and carrying out natural image classification. A Gibbs sampling method and a topic unbalance prior probability method are adopted in the image classification method, the number of the parameters is reduced, correlation of the topics is increased, representation effects of the topic distribution vectors on the images are better and image classification accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image classification method based on a supervised shared component topic model in the technical field of image classification. The invention can be used for target identification and detection, vehicle navigation and diagnosis of medical diseases. Background technique [0002] At present, natural image classification has become a very important research subject in the field of image processing technology. Natural image classification has a wide range of applications, such as target recognition and detection, vehicle navigation, diagnosis of medical diseases and other fields. Due to the different lighting conditions, shooting angles and other conditions, there will be certain differences within the natural image category, and due to the lack of image feature extraction methods, there will be certain consistency among the natural image categories, which lead to t...

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06V30/194G06F18/2411G06F18/24
Inventor 王爽焦李成陈阳平霍丽娜侯彪马文萍马晶晶张雪
Owner XIDIAN UNIV
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