Image retrieval algorithm based on distribution entropy gain loss function

A technology of gain loss and image retrieval, which is applied in digital data information retrieval, computing, computer components, etc., can solve problems such as lack of network parameters, and achieve the effect of enhancing accuracy, improving accuracy, and optimizing retrieval effect

Active Publication Date: 2019-10-11
JILIN UNIV
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Problems solved by technology

Although the pre-trained network has achieved amazing retrieval performance, it often does not have network parameters that match the image retrieval task, so image retrieval network fine-tuning has become a hot research topic

Method used

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  • Image retrieval algorithm based on distribution entropy gain loss function
  • Image retrieval algorithm based on distribution entropy gain loss function
  • Image retrieval algorithm based on distribution entropy gain loss function

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Embodiment Construction

[0026] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings, but it is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be covered by the scope of the present invention. within the scope of protection.

[0027] The present invention provides an image retrieval algorithm based on distribution entropy gain loss function, such as figure 1 As shown, the network training structure includes image feature extraction, contrastive loss function and feature vector distribution entropy, image feature extraction includes convolutional neural network structure, generalized mean pooling, and normalization, where:

[0028] The image feature extraction takes the training data set obtained by using the SfM algorithm as input, and outputs the feature vector ...

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Abstract

The invention discloses an image retrieval algorithm based on a distribution entropy gain loss function, and the algorithm employs a pre-training network for initialization, trains a network accordingto the demands of an image retrieval task, employs a self-designed distribution entropy gain loss function during the training of the network, and improves the accuracy of image retrieval. A distribution entropy gain loss function is combined with a comparison loss function and a relative entropy, so that the accuracy of image similarity measurement during network training is enhanced; the comparison loss function calculates the similarity between the features through the Euclidean distance, the relative entropy can be used for measuring the distribution difference between the feature vectors, and the relative entropy is supplemented to the comparison loss function to improve the similarity measurement of the feature vectors; a distribution entropy gain loss function is used for traininga network model, the network model more suitable for an image retrieval task is obtained by adjusting network parameters, and the trained network model obtains a better retrieval effect in an image retrieval experiment.

Description

technical field [0001] The invention belongs to the technical field of image retrieval and relates to an image retrieval algorithm for training a network through a distribution entropy gain loss function. Background technique [0002] With the vigorous development of Internet technology, social software is rich and diverse, and various forms of multimedia information are flooding our lives. How to quickly and accurately capture and effectively utilize multimedia information has become an important research topic, which has aroused widespread concern in the academic circles. Under this trend, image retrieval technology has been fully and comprehensively developed. [0003] In recent years, with the successful application of neural networks in image classification, researchers have paid more and more attention to the application of neural networks in the field of image retrieval. A large number of studies have shown that the features output by the convolutional layer of neura...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/583G06K9/62
CPCG06F16/583G06F18/214
Inventor 刘萍萍苗壮勾贵霞郭慧俐石立达金百鑫王振王慧龚柯
Owner JILIN UNIV
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