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Drawing image retrieval method and device

A technology of image retrieval and painting, applied in the direction of neural learning methods, special data processing applications, instruments, etc., can solve the problem that the retrieval accuracy is difficult to meet the actual needs, and achieve enhanced robustness, improved accuracy and robustness, and improved expression effect of ability

Inactive Publication Date: 2017-11-03
盛世贞观(北京)科技有限公司
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AI Technical Summary

Problems solved by technology

These features are easily affected by illumination and nonlinear deformation, making retrieval accuracy difficult to meet actual needs

Method used

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  • Drawing image retrieval method and device
  • Drawing image retrieval method and device
  • Drawing image retrieval method and device

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Embodiment Construction

[0029] Hereinafter, a preferred embodiment of the present invention will be described in detail. Examples of these preferred embodiments are illustrated in the drawings. The embodiments of the present invention shown in the drawings and described in accordance with the drawings are merely exemplary, and the technical spirit of the present invention and its main operations are not limited to these embodiments.

[0030] Here, it should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only the structure and / or processing steps closely related to the solution according to the present invention are shown in the drawings, and the Other details not relevant to the present invention.

[0031] At present, the deep learning technology based on deep convolutional neural network has extremely strong independent learning ability and highly nonlinear mapping, which makes it possible to design complex, high-precision and high-speed automatic ident...

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Abstract

The invention discloses a drawing image retrieval method. The method comprises the following steps of constructing a drawing image training sample set, wherein the sample set comprises a plurality of drawing image training samples corresponding to the same drawing works, and the image type of each drawing image training sample in the sample set is marked according to fine classification and rough classification; based on a depth convolution neural network, constructing a drawing image feature extractor, automatically learning fine classification features and coarse classification features from the training sample set, obtaining drawing image feature vectors and storing the drawing image feature vectors into a drawing image feature database; by utilizing the constructed drawing image feature extractor, extracting the feature vector of a to-be-retrieved drawing image; calculating the distance between the feature vector of the to-be-retrieved drawing image and each drawing image feature vector in the drawing image feature database; and obtaining an image retrieval result based on the distance. According to the invention, the accuracy, the accuracy, the robustness and the usability of the drawing image retrieval system are improved.

Description

Technical field [0001] The invention relates to the field of image retrieval, in particular to a painting image retrieval method and device. Background technique [0002] With the continuous improvement of people's living standards, art lovers increase year by year. Most art lovers have limited knowledge accumulation and urgently need effective tools to help them appreciate art. Suppose a fan has a painting, but he does not know the history and characteristics of the work. Fans can use image search technology to search for the works most similar to the work, so as to find relevant background knowledge and professional reviews. [0003] The current mainstream image retrieval methods are mainly divided into two categories: keyword-based methods and image content-based methods. The keyword-based image retrieval method searches for images related to similar text according to the text input by the user. Because it is difficult for users to accurately describe paintings, and the imag...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/62G06N3/08G06F17/30
CPCG06F16/583G06N3/084G06V10/443G06F18/214
Inventor 吕洪凤盛冬冬冯军
Owner 盛世贞观(北京)科技有限公司
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