Method and device for automatic image labeling based on label graph model random walk

Abstract

The invention provides a random walking image automatic annotation method and device based on a label graph model. The method comprises the following steps: providing an annotated image set and an image to be annotated; acquiring an adjacent image set related to the image to be annotated; acquiring a candidate label set; constructing a co-occurrence matrix; acquiring a typical vector; constructing a tendency matrix for the candidate label set according to the typical vector; fusing the co-occurrence matrix and the tendency matrix, so as to obtain a relation matrix; constructing a label graph model; and carrying out random walking on the label graph model, so as to obtain a weight vector of a node; and determining the label of the image to be annotated according to the corresponding weightvalue of each node in the weight vector. The method can be used for effectively annotating the images according to the co-occurrence relation and tendency relation between the labels; and the method has the advantage of accuracy in annotation; the image automatic annotation device has the advantages of being simple in structure and being easy to realize.

Description

technical field [0001] The invention relates to the field of multimedia technology, in particular to an image automatic labeling method and device based on a label graph model random walk. Background technique [0002] With the rapid development of social network and digital camera technology, the explosive growth of network image data, how to effectively store, manage and retrieve such a large amount of image data has become a severe challenge and an urgent need. Traditional image retrieval based on surrounding text (such as Google image search) cannot achieve good retrieval accuracy due to too much noise in the surrounding text, while image content-based retrieval (CBIR) technology cannot bridge the gap between the underlying image features and high-level semantics. The "semantic gap" (Semantic Gap) between them has not been widely recognized and applied. Research in recent years has shown that automatic annotation technology based on image semantic content will probably ...

AI Technical Summary

Problems solved by technology

[0006] The determination of the existing technology is that even if the existing automatic image labeling method is applied to the artificially constructed standard data set, the accuracy (precision) and recall (recall) can only reach about 30%, but in the actual data set, Due to the presence of larger label noise, the precision and recall will be lower

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