Hierarchical semantic description based image retrieving method

Abstract

The present invention provides a hierarchical semantic description based image retrieving method. The method comprises the following steps: obtaining a scenario category classifier by using an image set with a scenario category tag; obtaining a visual attribute classifier by using an image set with a visual attribute tag; performing classification on each image in a query database by using the scenario category classifier and the visual attribute classifier, so as to obtain a scenario category description and a visual attribute description of each image; performing classification on an input query image by using the scenario category classifier and the visual attribute classifier, so as to obtain a scenario category description and a visual attribute description of the input query image; and in the query database, screening out images with the same scenario category description as the scenario category description of the query image to form a candidate image set, and searching for an image similar to the visual attribute description of the query image in the candidate image set as a retrieval result. The method provided by the present invention adopts a hierarchical semantic description method with the scenario category and the visual attribute combined, and the method is high in semantic similarity of the retrieval result and high in the retrieval speed.

Description

technical field [0001] The invention relates to the technical field of image retrieval, and more specifically, relates to an image retrieval method based on hierarchical semantic description. Background technique [0002] In the era of social media, due to the explosive growth of shared images in network space and the demand for various multimedia applications on the Internet and mobile clients, it is a great challenge to quickly and accurately retrieve images that users care about from large-scale image databases. [0003] There are two problems in existing image retrieval methods: on the one hand, it is necessary to calculate the feature similarity between the query image and each image in the query database, and the search range is large; on the other hand, due to the relationship between the underlying features of image content and the high-level semantics There is a semantic gap problem (that is, the lack of correspondence between low-level features and high-level seman...

AI Technical Summary

Problems solved by technology

[0003] There are two problems in existing image retrieval methods: on the one hand, it is necessary to calculate the feature similarity between the query image and each image in the query database, and the search range is large; on the other hand, due to the relationship between the underlying features of image content and the high-level semantics There is a semantic gap problem (that is, the lack of correspondence between low-level features and high-level semantics), the feature similarity between the retrieved image and the query image is high, but the semantic similarity is poor, which cannot fit the user's retrieval intention

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