Image annotationlabeling method based on regional context relation deep learning

Abstract

The invention relates to an image annotation method for regional context deep learning, and aims to solve the problem that most of current universal image annotation methods are based on visual features, especially depth features of images, but when semantic keywords needing to be annotated are more and scattered, complex image annotation cannot be effectively solved only by means of the visual features. The invention provides an image annotation method based on thea regional context relation deep learning network. The method comprises the steps of automatically learning a context relationshipbetween regions in an image, fusing the context relationship into feature expression of the regions, accumulating features of the regions to serve as features of the image, and finally learning a multi-class support vector machine to perform semantic recognition on each image and sort contribution values of semantics to obtain annotation keywords. The method is flexible and convenient, can automatically learn the regional context relation of the image while achieving the image labeling, and is higher in practicality.

Description

technical field [0001] The invention relates to the fields of automatic learning of image features and pattern recognition, in particular to a method for learning deep features of image context perception and semantically annotating images. Background technique [0002] With the popularization of consumer electronics such as computers and the rapid development of Internet technology, the number of images in social media networks has increased geometrically, and a large number of images pose severe challenges to tasks such as data retrieval and classification. Quickly understanding images is an important prerequisite for content monitoring and management of network images, and image annotation, as a key technology for image understanding, has attracted more and more attention and research. [0003] Image annotation is a method for describing image content, and its purpose is to describe an image with one or more semantic keywords. Due to the high cost of manually labeling im...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the purpose of the present invention is to provide an automatic image labeling method that fuses the contextual relationship of the image area to solve the problem that the depth feature cannot effectively express the structural information of the object. Deep features to improve image annotation performance

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