Retrieval Method Using Random Quantized Vocabulary Tree and Image Retrieval Method Based on It

Abstract

The invention discloses a retrieval method using a random quantization vocabulary tree and an image retrieval method based on the random quantization vocabulary tree. The method comprises the steps that (1) a nearest neighbor search tree is generated, and all feature vectors of a whole database are used as root nodes of a first segment for downward segmentation; (2) at the second level, k points are randomly selected from the whole database to serve as cluster centers, then each feature vector is distributed to the cluster center closest to the feature vector according to a selected similarity measurement method, the whole database is divided into k subsets, and downward segmentation is continued; (3) at the third level, k feature points are randomly selected from a feature vector pool of all k clusters obtained from the second level to serve as cluster centers of the next level; and (4) the steps are repeated. Through the image retrieval method, the problem that in the prior art, vocabulary tree establishment needs a large amount of time is solved, the vocabulary tree can be established in a short time, and the real-time requirement is met.

Description

technical field [0001] The invention relates to the technical field of image retrieval, in particular to a retrieval method using a random quantized vocabulary tree and an image retrieval method based thereon. Background technique [0002] In recent years, with the development and popularization of digital technology, especially network technology, the Internet of Things and the development of computer information collection software and hardware technology, more and more data has been collected and stored, and the speed of data collection has far exceeded that of traditional methods. The speed at which they can be handled, and this trend is becoming more and more obvious. Facebook is the world's leading photo sharing site. As of November 2013, about 350 million photos are uploaded every day, and the photo capacity on Facebook alone has reached 250PB; in terms of digital video, YouTube's 2013 statistics show , more than 72 hours of video content is uploaded every minute, an...

AI Technical Summary

Problems solved by technology

Its obvious disadvantage is that neither computer vision nor artificial intelligence technology can automatically label images with text, and it needs to rely on manual labeling

Nister and Stewenius proposed a vocabulary tree-based retrieval method that has good retrieval results in high-dimensional spaces, but it takes a long time to build trees in high-dimensional spaces, making it difficult to meet the timeliness requirements of modern databases.

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