Image retrieval method

An image retrieval and image technology, applied in the field of image processing, can solve the problems of error-prone, affect the accuracy of image retrieval, manual annotation workload, etc., to achieve the effect of accurate image retrieval

Active Publication Date: 2016-06-01
北京八月瓜科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an image retrieval method to solve the problem that the image retrieval accuracy is affe

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[0050] Example

[0051] (1) Use open source tools such as VLFeat to extract SIFT feature points from each checked image in the checked image library, and perform L2 normalization processing on the SIFT feature points (that is, the L2 modulus length of the SIFT feature points becomes 1), and random sampling Part of the feature points, and use the K-Means method to train D cluster centers, all cluster centers form a D-dimensional dictionary;

[0052] (2) Use the D-dimensional dictionary obtained in the previous step to describe the features of the searched image and the query image, and obtain the D-dimensional feature vector of the searched image and the query image respectively. Let Q be the feature vector of the query image, I i (i=1,2,...,N) is the feature vector of the checked image;

[0053] (3) Use the convolutional neural network AlexNet to extract the 4096-dimensional image features of the last fully connected layer of the checked image, and perform feature description to form...

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Abstract

The invention discloses an image retrieval method. The image retrieval method comprises the following steps: carrying out feature description on a queried image and a querying image; carrying out deep learning on the queried image and the querying image; carrying out similarity measurement on the queried image by using features of the querying image to obtain a feedback list sequenced according to similarity; training a sample selection and classification device by using the querying image and images in the feedback list; carrying out classification prediction on front n(minute) images and pseudo negative example images by using the sample selection and classification device, and taking g images which are closest to a classification face; marking the front m images in the feedback list and the g images obtained in the previous step to obtain positive images and negative images; fusing features of the positive images and the querying image, and carrying out similarity measurement again on the queried image by using the fused features to obtain a final sequencing result. According to the image retrieval method disclosed by the invention, a query expansion method in image retrieval is realized by using an active learning method, and more accurate image retrieval can be realized on the premise of marking to a little amount of users.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image retrieval method. Background technique [0002] At present, content-based image retrieval methods have been more and more widely used, and query expansion method is one of the most effective methods to improve its query performance, and choosing a good expanded image is an important step in query expansion method. The existing extended image selection method is based on the first query, and the extended image is selected through the geometric verification technology based on feature points. The extended image selection performed by this method has problems such as single extended image features and error-prone. The traditional image retrieval technology based on correlation feedback focuses on constructing a better retrieval model by utilizing the results of multiple correlation feedbacks. They generally require multiple feedbacks and a relatively large amount of manual...

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/583G06F18/2113G06F18/2155G06F18/217G06F18/24
Inventor 赵鑫李长青孙鹏
Owner 北京八月瓜科技有限公司
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