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Image segmentation hash sorting method based on deep learning

A piecewise hashing and deep learning technology, applied in the field of image piecewise hash sorting based on deep learning, can solve the problems of insufficient Hamming distance and large calculation overhead, and achieve the expansion of similarity, less calculation loss, and rich semantics information effect

Pending Publication Date: 2022-03-08
SUN YAT SEN UNIV
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  • Application Information

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Problems solved by technology

However, since the distance measure of traditional binary hash codes uses Hamming distance, Hamming distance is enough to solve the retrieval problem based on classification labels in the past, because it only needs to distinguish like or unlike, and only two similarities are needed. Yes, but when the problem is extended to a sorting problem that requires more similarities, the Hamming distance is somewhat insufficient. For example, to solve the sorting of 100 categories, 100 similarities are required, and at least 99 bits of hash code are required to 100 Hamming distances are calculated, however longer hash codes bring greater computational overhead

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  • Image segmentation hash sorting method based on deep learning
  • Image segmentation hash sorting method based on deep learning
  • Image segmentation hash sorting method based on deep learning

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Embodiment Construction

[0038] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0039] In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0040] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0041] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] Such as Figure 1-2 As shown, an image segment hash sorting method based on deep learning includes the following steps:

[0043] S1: Establish a deep learning network model G for image hash code extraction;

[0044] S2: Use the new piecewise hash metric to calculate the distance of the hash code;

[0045] S3: Train and test the model with a new ranking loss function;

[0046] S4: Es...

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Abstract

The invention provides an image segmentation Hash sorting method based on deep learning, and the method comprises the steps: extracting a Hash code which can be used for solving a sorting problem of multivariate similarity through a G network, and carrying out the meticulously designed multi-segment Hash distance measurement and a dense triple loss function, on the premise of not increasing the length of the Hash code, the expressible similarity is expanded, so that the Hash code has richer semantic information, and less calculation loss is kept in the sorting problem.

Description

technical field [0001] The present invention relates to the fields of computer application technology and computer vision, and more specifically, relates to a method for sorting image segmentation hashes based on deep learning. Background technique [0002] In recent years, with the rapid development of the Internet, the Internet has become the main way for people to entertain and obtain information. However, it is still lacking in using images for retrieval. Image retrieval technology can help people find other images related to a certain image. For example, when shopping online, they want to find clothes similar to a certain piece of clothing, but it is difficult to describe them in words. At this time, online shopping platforms usually provide image search The user only needs to provide the image of the desired product, and the system will automatically sort it in the database to find similar products for the user to choose. Therefore, image retrieval technology has a gr...

Claims

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Application Information

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IPC IPC(8): G06V10/774G06V10/74G06K9/62G06N20/00
CPCG06N20/00G06F18/22G06F18/214
Inventor 赖韩江龚秦康潘炎印鉴
Owner SUN YAT SEN UNIV