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APP similar icon retrieval method and system based on convolutional neural network

A convolutional neural network and retrieval system technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of low retrieval accuracy and low retrieval efficiency, and achieve the goal of overcoming low accuracy and ensuring high efficiency. Effect

Inactive Publication Date: 2017-09-29
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

This solves the technical problems of low retrieval efficiency and low retrieval accuracy in the prior art

Method used

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  • APP similar icon retrieval method and system based on convolutional neural network
  • APP similar icon retrieval method and system based on convolutional neural network
  • APP similar icon retrieval method and system based on convolutional neural network

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[0031] In order to make the purpose, technical invention and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0032] Such as figure 1 As shown, a convolutional neural network-based APP similar icon retrieval method, including:

[0033] (1) For the sample APP icon, the sample feature vector is extracted based on the convolutional neural network, and the sample file identification of the sample feature vector is recorded, and the sample feature vector is divided into N sample parts, and an index is est...

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Abstract

The invention discloses an APP similar icon retrieval method and system based on a convolutional neural network. The method comprises the steps that a sample feature vector, sample file identification and N indexes are saved in the retrieval system; for target APP icons, a target feature vector is uniformly divided into N target parts, combination is conducted on each target part, and M target combined feature vectors are obtained; retrieval is conducted M times in the retrieval system for M combined feature vectors, M file identification collections are obtained, a unit is taken from M file identification collections, linear computation is conducted in the unit, the similarity of sample APP icons and the target APP icons is obtained, and the sample APP icons are sorted by means of the similarity. Accordingly, the defects that in traditional image retrieval, the accurate rate is low, and the retrieval efficiency is low are overcome; meanwhile, great convenience is brought to a user, and corresponding cellphone application programs can be sought according to the icons.

Description

technical field [0001] The invention belongs to the field of data retrieval, and more specifically relates to a convolutional neural network-based APP similar icon retrieval method and system. Background technique [0002] With the development of the Internet and the increase in the penetration rate of smart phones, handheld mobile terminals have been greatly developed, accompanied by an increase in the number of mobile phone applications. For users and security inspectors, mobile phone application icons can be used to It is very difficult to quickly search for corresponding or similar mobile phone applications. For this, image retrieval techniques need to be used. [0003] Current image retrieval techniques mainly focus on text-based retrieval and content-based retrieval. Text-based retrieval refers to establishing keywords or text titles and some additional information for image files to describe the image, and then linking the storage path of the image with the keyword ...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06N3/08
CPCG06N3/08G06F16/532G06F16/5838
Inventor 路松峰彭元波王同洋
Owner HUAZHONG UNIV OF SCI & TECH
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