Image retrieval method on basis of multi-feature fusion

Abstract

The invention discloses an image retrieval method on the basis of multi-feature fusion, which is mainly used for solving the problem of low accuracy of retrieving an image in the prior art. The image retrieval method comprises the following implementing steps: (1) extracting three visual features and semantic attribute features of all images in an image set to be retrieved; (2) calculating an adjacent distance matrix among all the images in the image set to be retrieved; (3) carrying out rough retrieval on the inquired images in each feature channel; (4) according to a rough retrieval result of each feature channel, analyzing the semantic attribute features of the images in the rough retrieval result and selecting a reference image; (5) according to the selected reference image, calculating each feature fusion template matrix; (6) according to the obtained fusion template matrice, obtaining a fused distance measure matrix; (7) according to the obtained distance measure matrix, returning a retrieval result to a user. The image retrieval method obviously improves accuracy of final image retrieval and can be used for image retrieval.

Description

technical field [0001] The invention belongs to the technical field of information retrieval, and specifically relates to an image retrieval method based on image multi-feature fusion, which can be used in the field of Internet image retrieval. Background technique [0002] Under the current Internet background, most commercial network image search engines such as Google, Image Search, and Bing use text-based retrieval technology, mainly using annotations such as titles and description text around images to make similar queries to query text. Sex matching, using this similarity to retrieve the retrieved images. However, techniques based solely on text retrieval often fail to achieve good results due to the semantic gap between text and image content. In addition, due to the presence of ambiguous noise data in image annotation, the correctness of image labels derived from image metadata analysis is often not guaranteed. [0003] In order to improve the accuracy of existing ...

AI Technical Summary

Problems solved by technology

Although today's image retrieval methods based on multi-feature fusion can improve the accuracy of image retrieval and ranking results to a certain extent, essential problems still exist.

Different retrieval situations correspond to different importance of visual features, and it is often difficult to obtain satisfactory image retrieval results by directly performing multi-feature fusion with fixed coefficients.

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