Digital image multi-semantic annotation method based on spatial dependency measurement

Abstract

The invention belongs to a digital image multi-semantic annotation method which is characterized by comprising the following steps in sequence: (1) inputting a plurality of digital images with known semantemes and all digital images to be annotated into a computer; (2) acquiring a characteristic vector set of all images by extracting characteristics; (3) establishing a mark vector of marked images and a final mark vector set of all images; (4) calculating a Gram matrix of the characteristic vector set; (5) acquiring a measurement value of the dependency degree of the characteristic vector set and the mark vector set by using a spatial dependency measurement method; (6) gradually increasing the dependency measurement value to the maximum in the iterative process, thereby obtaining confidence values that the images to be annotated belong to semantemes; and (7) setting a threshold, and judging the semantemes of the images to be annotated. The digital image multi-semantic annotation method has the advantages that firstly, the annotation effect can be improved by adopting a great number of images which are not semantically annotated, secondly, the method is applicable to the situation of multi-semantic annotation situation, and thirdly, the calculation speed is relatively high.

Description

Technical field [0001] The invention relates to a semi-supervised multi-semantic labeling method for digital images based on spatial dependence measurement, and belongs to the field of electronic information technology. Background technique [0002] Image semantic annotation aims to use semantic keywords to represent the semantic content of an image, which is very important for image analysis and understanding and image retrieval. Early image semantic annotation requires professionals to manually mark keywords based on the semantics of each image, which is time-consuming and subjective. In order to overcome these shortcomings of manual annotation, researchers have proposed many methods to automatically annotate the semantic content of images in recent years, including translation models based on generative models, cross-media related models, and asymmetric support vector machines and hierarchies based on discriminant models. Classification and other methods. Generally, these me...

AI Technical Summary

Problems solved by technology

At present, although semi-supervised learning methods have been greatly developed, and many methods including TSVM and graph semi-supervised learning have been proposed, semi-supervised learning methods that can be applied to multi-semantic (multi-label) learning problems are still relatively rare.

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