Retrieval method using random quantization vocabulary tree and image retrieval method based on random quantization vocabulary tree

A random quantization and vocabulary tree technology, which is applied in still image data indexing, still image data retrieval, special data processing applications, etc., can solve high-dimensional space tree building time is long, relying on manual labeling, difficult to meet the requirements of timeliness of retrieval, etc. problems, to achieve the effect of meeting real-time requirements and quickly extracting image features

Active Publication Date: 2017-12-08
XI AN JIAOTONG UNIV
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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 tre...

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  • Retrieval method using random quantization vocabulary tree and image retrieval method based on random quantization vocabulary tree
  • Retrieval method using random quantization vocabulary tree and image retrieval method based on random quantization vocabulary tree
  • Retrieval method using random quantization vocabulary tree and image retrieval method based on random quantization vocabulary tree

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Embodiment Construction

[0038] Hereinafter, this solution will be further described in conjunction with the drawings and embodiments.

[0039] Such as figure 1 As shown, the retrieval method using random quantization vocabulary tree includes the following steps:

[0040] (1) Generate a nearest neighbor search tree, take all the feature vectors of the entire database as the root node of the first section, and divide it down;

[0041] (2) In the second level, randomly select k points from the entire database as the center of the cluster, and then assign each feature vector to the nearest cluster center according to the selected similarity measurement method, and divide the entire database into For k subsets, continue to sub-sections;

[0042] (3) In the third level, for each of the k clusters obtained from the second level, randomly select k feature points from their feature vector pool as the cluster centers of the next level, and then use the similarity measure The method assigns each feature vector to the ...

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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 field of image retrieval technology of the present invention particularly relates 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 development of the Internet of Things and computer information collection software and hardware technologies, more and more data is collected and stored, and the speed of quantity collection has far exceeded traditional methods. The speed at which they can be processed, 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 were uploaded every day, and the photo capacity on Facebook alone has reached 250PB; in terms of digital video, YouTube’s 2013 statistics show , Uploading more than 72 hours of video content every minute, there a...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/51G06F16/583G06F16/5838
Inventor 王晓春
Owner XI AN JIAOTONG UNIV
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