Multilevel content description-based image classification method

Abstract

The invention provides a multilevel content description-based image classification method. The method comprises the following steps of: 1) presetting a training image set; obtaining each image area hiberarchy tree by multilevel image segmentation; and extracting low-level features of each node area in the image area hiberarchy tree; 2) structuring a visual vocabulary by a low-level feature set ofa training image set area; mapping the image area hiberarchy tree to middle-level image features according to the visual vocabulary to obtain multilevel content description of a training image set; and 3) establishing an image classification model based on the multilevel content description of the training image set; and realizing the classification of images to be classified according to the image classification model. In the method, a multilevel segmentation area of the images is adopted; on one hand, the completeness of the image content description is enhanced; and on the other hand, the robustness of the over-segmentation and the under-segmentation of the images are enhanced. Therefore, more effective image description can be obtained to achieve higher image classification accuracy.

Description

technical field [0001] The invention relates to the field of pattern classification of images, in particular to an image classification method based on multi-level content description. Background technique [0002] With the popularization of electronic devices such as digital cameras and the advancement of image coding technology, visual content is created at a rate of millions of pieces every day. With the development of the Internet and the improvement of computer data processing capabilities, various resources on the Internet are also becoming more and more abundant. The problem people face is no longer the lack of multimedia content, but how to find the information they need in a large number of multimedia resources. In the absence of text annotation, content-based image classification can provide semantic clues for image content, which can promote efficient image retrieval and processing, and has very important research and application value. [0003] As the basis of ...

AI Technical Summary

Problems solved by technology

However, the image contains multi-level content from the whole to the local, and it is difficult to describe the multi-level content of the image based on a single-level image segmentation region.

In addition, because the unified segmentation termination condition is difficult to adapt to different images to be segmented, over-segmentation and under-segmentation often occur, and both over-segmentation and under-segmentation will reduce the ability to describe image content based on a single-level image segmentation method

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