Image classification method based on low-rank optimization feature dictionary model
A technology of feature dictionary and classification method, which is applied in the field of image processing, can solve problems such as unrealizable, low distinguishability of feature description, unfavorable image classification, etc., and achieve the effect of improving accuracy
Inactive Publication Date: 2014-04-30
重庆市国土资源和房屋勘测规划院 +1
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This method does not use the category information of the image when constructing the feature dictionary, and it cannot realize that the feature description formed by the dictionary when describing the same type of image is more similar, so the feature description formed is not highly distinguishable, which is not conducive to image classification
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[0014] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific implementation examples and with reference to the accompanying drawings.
[0015] The invention utilizes low-rank constraints to construct a feature dictionary, and proposes an image classification method that effectively improves the classification effect of a dictionary model. figure 1 Shown is the image classification framework based on the low-rank optimized feature dictionary image classification method of the present invention, which mainly includes image multi-level sub-block division, feature point detection and descriptor description in sub-blocks, low-rank optimized feature dictionary construction, and sparse constraint vector quantization , max-pooling normal vector merging, and utilizing classifiers to classify several modules. Each module can be divided into two parts, ...
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The invention provides an image classification method based on a low-rank optimization feature dictionary model. The method includes the following image classification steps that firstly, feature points of images are monitored and features of the feature points are described; secondly, a space pyramid with L layers is built, and each layer of images are divided into 2<L-1>subblocks; thirdly, all the subblocks are described through a prebuilt feature dictionary, dictionary expression coefficients are connected in series, and feature vectors for describing the images are formed, wherein a sparse coding mode is adopted for the vector quantification method; fourthly, the feature vectors acquired through the building method are utilized to train a classifier, and the classifier is utilized to classify feature vectors of new images. In order to improve capacity of the feature dictionary for describing the features of the images, the feature dictionary is built through the low-rank optimization method, and then the feature vectors formed when the feature dictionary is utilized to describe the images of the same kind are more similar. The experimental result on an open beta database in two fields proves effectiveness of the method.
Description
technical field [0001] The invention belongs to the field of image processing, and in particular relates to an image classification method based on a low-rank optimized feature dictionary model. Background technique [0002] With the rapid development of computer technology, the Internet and digital technology, more and more image and video equipment are integrated into people's daily life and work, making more and more image information available, and the data volume of multimedia data is increasing. Incredibly fast growth. How to make better use of this information more quickly and make it serve people's daily work and life is a very meaningful research work. Image classification is a prerequisite for many practical applications, such as satellite remote sensing image classification, natural scene analysis, content-based image / video search, etc. Automatic image classification is a technology that uses computer programs to automatically analyze image content to identify i...
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Inventor 马泽忠吕煊李爱迪彭海涛李爱美
Owner 重庆市国土资源和房屋勘测规划院