Image retrieval method based on multi-feature fusion

Abstract

The invention provides an image retrieval method based on multi-feature fusion. The image retrieval method is used for solving the problem that an image retrieval method based on a single feature cannot meet the query requirement of a user. The method comprises the steps of performing noise reduction processing on a to-be-retrieved image by utilizing a filtering method; performing feature quantification by utilizing the improved HSV color space to extract global features of the to-be-retrieved image; performing multi-scale morphological gradient extraction on the denoised image to extract local features of the to-be-retrieved image; performing adaptive fusion on the global features and the local features to obtain an adaptive fusion image; carrying out hash coding on the self-adaptive fusion image, calculating the similarity between the to-be-retrieved image and all the images in the database through Hash codes, and selecting the first several images with the highest similarity with the to-be-retrieved image as retrieval results of the to-be-retrieved image. According to the method, the feature points of the image are fully extracted, and the edge information of the image is protected more comprehensively in the local feature extraction process, so that the retrieval accuracy is improved, and the retrieval time is shortened.

Description

technical field [0001] The invention relates to the technical field of image retrieval, in particular to an image retrieval method based on multi-feature fusion. Background technique [0002] With the widespread application of next-generation information technologies such as the Internet of Things, big data, and mobile Internet, multimedia data is currently showing a geometric growth trend, which has a profound impact on people's daily study, work, and life. How to quickly and accurately find the desired target in massive image data has become one of the hotspots in the field of computer research. Content-based image retrieval was proposed in the 1990s. The main workflow is to extract low-level features such as image color, texture, and shape, map the low-level features to image feature vectors and feature libraries, and query images through retrieval. Compare the feature vectors in the feature library, calculate the similarity between images, and return the search results ...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the technical problem that the image retrieval method based on a single feature cannot meet the user's query requirements for the existing massive multimedia data, the present invention proposes an image retrieval method based on multi-feature fusion, which can effectively improve the performance of image retrieval. Good robustness in retrieval

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