Image library construction method, image retrieval method, image library construction device, image retrieval device and related equipment

Abstract

The invention discloses an image library construction method and device, an image retrieval method and device, computer equipment and a storage medium. The method comprises the steps that an image high-dimensional feature set is acquired; performing clustering processing on all high-dimensional features until a loss function converges to obtain at least two clusters; constructing an inverted file system based on all clusters; for each cluster, carrying out edge connection processing on all points in the cluster to obtain a complete graph corresponding to the cluster, and taking all the complete graphs as a current clustering graph; performing clustering processing on the current clustering graph to obtain at least two cluster centers; constructing an intermediate graph based on all cluster centers; taking the intermediate graph as a current clustering graph, returning to the step of carrying out clustering processing on the current clustering graph to obtain at least two cluster centers, and continuously executing until the number of layers corresponding to the intermediate graph reaches a preset number of layers, so as to obtain a multi-layer skip list; and constructing an image library based on the inverted file system and the multi-layer skip list. By adopting the method and the device, the image retrieval accuracy is improved.

Description

technical field [0001] The invention relates to the field of image retrieval, in particular to an image library construction method, an image retrieval method, an apparatus, a computer device and a storage medium. Background technique [0002] With the development of image processing, high-dimensional feature vectors are used more and more frequently. For example, in image processing, a model is used to extract features from an image to obtain a high-dimensional feature vector, which is used to represent the features of the image. . In the prior art, similar images can be obtained by storing high-dimensional feature vectors and using the high-dimensional feature vectors for image retrieval. At present, the commonly used methods for retrieving images through high-dimensional feature vectors include retrieval based on quantization coding, retrieval based on trees, etc. However, retrieval based on quantization coding reduces the retrieval accuracy due to the compression of hig...

AI Technical Summary

Problems solved by technology

At present, the methods commonly used to retrieve images through high-dimensional feature vectors include retrieval based on quantization coding and tree-based retrieval. However, the retrieval accuracy based on quantization coding is reduced due to the compression of high-dimensional feature vectors. The retrieval is suitable for retrieval vectors with low dimensionality and small amount of data. When there are a large number of high-dimensional feature vectors, the accuracy of retrieval will decrease.

[0003] Therefore, when there are a large number of high-dimensional feature vectors, there is a problem of low accuracy in image retrieval.

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