Image retrieval method based on incremental learning of large vocabulary tree

An incremental learning and image retrieval technology, applied in still image data retrieval, still image data indexing, special data processing applications, etc., which can solve the problems of high training time cost and long training time.

Inactive Publication Date: 2017-01-18
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

At present, due to the limitation of computer development level, the CBIR system cannot really support semantic-based image retrieval. In order to improve the efficiency of matching, it takes a lot of time to train the feature vectors in the process of indexing to achieve fast retrieval. Effect
The image retrieval technology based on the vocabulary tree is an effective way, which can quickly find the retrieval results in millions of pictures, but its defect is that the training time is too long, and the training time for millions of pi

Method used

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  • Image retrieval method based on incremental learning of large vocabulary tree
  • Image retrieval method based on incremental learning of large vocabulary tree
  • Image retrieval method based on incremental learning of large vocabulary tree

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Embodiment 1

[0041] Step 1, extract the SIFT features of all pictures in the large-scale image database, the capacity of the large-scale image database is defined as 1 million, and the large-scale image database used is MIR-FLICKER-1M, Figure 4 For the sample image of the selected large-scale image library, 200 SIFT feature points are extracted from each image to obtain a descriptor set;

[0042] Step 2, construct a tree data structure with L layers and K branches. K-means clustering is performed on these SIFT feature descriptors, and the cluster centers are put into the nodes of the vocabulary tree as visual words. The formula for calculating the number of nodes M of the vocabulary tree is as follows, where L=6, K=10:

[0043] M = Σ i = 1 L K i = K L + 1 -...

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Abstract

The invention discloses an image retrieval method based on incremental learning of a large vocabulary tree. The generated large training picture vocabulary tree which contains millions of images is used as a dictionary, leaf node number information associated with feature descriptors of newly added pictures is extracted from the vocabulary tree so as to form a vector for description, the incremental learning of the newly added pictures is completed, leaf node number information associated with feature descriptors of to-be-queried pictures is extracted in a same mode as the incremental learning so as to form a vector, and the two leaf node information vectors are compared so as to find out an image represented by the vector with a highest contact ratio with the leaf node number information of the to-be-queried pictures as a retrieval result to be returned. The image retrieval method has the advantages of high accuracy of the retrieval result and good robustness, and a real-time image retrieval task of a database increment can be satisfied.

Description

technical field [0001] The invention belongs to the technical field of image retrieval, in particular to an image retrieval method based on incremental learning of large-scale vocabulary trees. Background technique [0002] In recent years, with the rapid development of the mobile Internet and the popularization of mobile terminals, people can take and share their own pictures as they like. Through the retrieval and identification of pictures, user behavior can be analyzed to provide data support for various industries. Such as: understanding customers, meeting customer service needs, optimizing machine and equipment performance, improving safety and law enforcement, improving urban transportation, financial transactions, etc. Therefore, image retrieval and recognition technology has extremely important application value. Therefore, quickly and effectively managing and retrieving valuable information has quickly become an urgent need for people, and then Content-Based Imag...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/51G06F16/583
Inventor 李静韩世伟杨涛张念曾
Owner XIDIAN UNIV
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