Spatial local clustering description vector based image classification method

Abstract

The invention discloses a spatial local clustering description vector based image classification method which mainly solves the problem of lack of feature point space distribution information in image description vectors in the prior art. The method includes the steps: (1) extracting 'scale-invariant feature transformation' feature points of all images; (2) in a feature point space of images in a training set, clustering the obtained feature points by the aid of a mean clustering algorithm to obtain a code book; (3) utilizing difference vectors for generating a local clustering description vector of each image in an image set; (4) performing 2X2 space region division on each image, and making statistics on number of feature points and coordinates of each cell block; (5) utilizing the local clustering description vector of each cell block for stitching to generate a spatial local clustering description vector of each image; and (6) utilizing a support vector mechanism for constructing a classification hyperplane to achieve image classification. The method has the advantages that image information can be described accurately, accuracy rate of image classification is improved, and the method can be used for large-scale image classification and retrieval system construction.

Description

technical field [0001] The invention relates to the technical field of image processing, and relates to an image classification method, which can be used for intelligent image classification management and web image push. Background technique [0002] Image classification is a typical problem in the field of computer vision, and it is particularly prominent with the massive growth of multimedia data. Image classification is usually based on the semantic content of the image, such as specific scenes, specific inclusions, etc., to add different category labels to the image to achieve image classification. Since images are often affected by imaging factors such as viewing angle, illumination, and occlusion, it brings great challenges to image classification. [0003] At present, image classification methods mainly include image classification methods based on text labels and image classification methods based on content. [0004] Image classification methods based on text lab...

AI Technical Summary

Problems solved by technology

The main disadvantage of this patent is that the constructed frequency histogram is actually an unordered set of local feature vectors. Using this unordered set to vectorize an image does not consider the spatial structure and layout information of feature points at all. , for images with obvious hierarchical characteristics, the spatial layout information of feature points may contain information that is decisive for classification, ignoring this information severely limits the ability of the frequency histogram to describe the image

[0006] Another image classification method widely used in theoretical research is "Fisher kernels on visual vocabulary for image categorization., F.Perronnin and C.Dance, CVPR, 2007", which adds the kernel method to the BOF method The idea of ​​constructing the frequency histogram of image feature points in the visual dictionary, although this method has achieved a good classification effect to a certain extent, it does not consider the spatial distribution of feature points, resulting in the generated frequency histogram. The components are completely disordered, and it is impossible to describe the position range of the corresponding object in the image space in the image

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