Nature scene image classification method based on area dormant semantic characteristic

Abstract

The invention discloses a method for the classification of natural scene images on the basis of regional potential semantic feature, aiming at carrying out the classification of the natural scene images by utilizing the regional potential semantic information of the images and the distribution rule of the information in space. The technical proposal comprises the following steps: firstly, a representative collection of the classification of the natural scene images is established; secondly, sampling point SIFT feature extraction is carried out to the images in the representative collection of the classification of the natural scene images to generate a general visual word list; thirdly, the regional potential semantic model of an image is produced on the representative collection of the classification of the natural scene images; fourthly, the extraction of the regional potential semantic feature of the image is carried out to any image; finally, a natural scene classification model is generate, and classification is carried out to the regional potential semantic feature of the image according to the natural scene classification model. The method inducts the regional potential semantic feature, thus not only describing the regional information of image sub-blocks, but also including the distribution information of the image sub-blocks in space; compared with other methods, the method of the invention can obtain higher accuracy, and no manual labeling is needed, thus having high degree of automation.

Description

technical field [0001] The present invention relates to a method for image classification in the technical field of multimedia information processing, especially a method for classifying natural scene images by extracting regional latent semantic features of images, which is a method that comprehensively considers the information contained in image regions And the method of classifying natural scene images based on the spatial distribution of these regional information in the image. Background technique [0002] The rapid development of the Internet, the development of information storage and transmission technology, and the popularization of digital devices have brought about an explosive growth of image data, which poses new technical challenges to how to retrieve and browse a large amount of image data. Faced with such a huge amount of image data, the traditional management method of relying on manual classification of images has become unfeasible because it requires a lo...

AI Technical Summary

Problems solved by technology

Such methods can greatly reduce the need for manual annotation, but they usually classify scenes based on the overall appearance of visual words in images. They neither consider the distribution characteristics of visual words in space, nor can they use regional semantics in images. Therefore, it is often impossible to obtain good classification results when the overall distribution of image visual vocabulary is not obvious.

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