Image labeling method

Abstract

The invention relates to an image labeling method, comprising the following steps: clustering each label sample in a data set by using an improved FCM clustering algorithm, dividing data sets with different semantic into different classes, and obtaining a cluster center set of each class; calculating an Euclidean distance from an image to the cluster center of each class, calculating to obtain theaverage distance from the image to each class, solving the class which has the smallest distance from the image, using the class as a labeling class of the image; finding the class having the nearestdistance from the image to the cluster center of the class, counting a labeling word which have highest occurrence number in the class, using the labeling words as the labeling word of the image. Theimage labeling method uses an improved FCM clustering algorithm to cluster each semantic label class, and a new distance measurement algorithm is usedd in the improved FCM clustering algorithm, thereby greatly improving the accuracy of image labeling. The method has good labeling effect and can preferably meet needs of practical application.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image labeling method. Background technique [0002] In recent years, with the rapid development of computer technology and storage technology, as well as the popularization of digital image information and the Internet, a large amount of data is generated every day through major websites and digital products, and is transmitted on the Internet at an extremely fast speed. spread. A large amount of irregular data needs to be distinguished and processed, and then the storage of these data needs to be identified and standardized for more convenient application. This creates a difficult problem on how to efficiently retrieve and process these piles of data. Automatic image annotation technology (Automatic Image Annotation, AIA) is a good solution to the semantic problems of these labeled images, by automatically marking these images with keywords to reflect ...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, the commonly used image annotation method is the image annotation method based on the fuzzy c-means (abbreviated as FCM) clustering algorithm. The traditional FCM clustering algorithm uses the traditional Euclidean distance as the similarity, with The difference between the value of the image feature and the cluster center is used as the similarity between the objective function and the same kind of samples, the accuracy of the classification is not high, and the boundaries of the classification are relatively blurred, which makes the distinction between classes not obvious enough, which leads to the existing technology. The image labeling method based on the traditional FCM clustering algorithm has low labeling accuracy and poor labeling effect, and it is urgent for those skilled in the art to improve the labeling method

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