Method and device for automatic image labeling based on non-equal probability random search of directed graphs

Abstract

The invention discloses an image automatic annotation method based on digraph unequal probability random search, which comprises the following steps: inputting an image to be annotated and an annotated image set; extracting a plurality of feature vectors of the image to be annotated; selecting an adjacent image set; constructing a digraph model of the image to be annotated; calculating a word similarity matrix Se between tags and a symbiotic relationship matrix Co between tags; fusing the word similarity matrix Se between tags and the symbiotic relationship matrix Co between tags, so as to obtain a tag similarity matrix TT; and carrying out unequal probability random search on each candidate tag in a candidate tag set in the digraph model, so as to calculate the score, and obtaining a plurality of high-score candidate tags to be used as the label results. The invention also discloses an image automatic annotation device based on digraph unequal probability random search. In the invention, the dependency relation between images and similarity relation between tags are utilized fully and reasonably, thus the image automatic annotation can be effectively carried out, and the annotation effect is better.

Description

technical field [0001] The invention relates to the field of computer multimedia technology, in particular to an image automatic labeling method and device based on directed graph non-equal probability random search. 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 sem...

AI Technical Summary

Problems solved by technology

[0011] From the perspective of existing technologies, even if the existing automatic image labeling methods are applied to the artificially constructed standard data set, the precision and recall rate can only reach about 30%, while in the actual data set, Due to the presence of larger label noise, the precision and recall will be lower

It can be seen that the automatic image annotation technology is far from meeting the actual needs.

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