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Method for intelligent recommendation of 3D picture based on deep learning and transfer learning

A technology of transfer learning and deep learning, applied in the field of intelligent recommendation of 3D paintings based on deep learning and transfer learning, can solve the problems of inability to independently design 3D paintings, lack of experience of new painters, etc., to save training time, avoid design difficulties, Avoid the effects of long design cycles

Active Publication Date: 2017-08-18
HUAQIAO UNIVERSITY +1
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AI Technical Summary

Problems solved by technology

Some novice painters are also unable to independently design 3D paintings due to lack of experience, which has become a vacancy in the 3D painting industry

Method used

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  • Method for intelligent recommendation of 3D picture based on deep learning and transfer learning
  • Method for intelligent recommendation of 3D picture based on deep learning and transfer learning

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Embodiment Construction

[0026] Examples, see figure 1 , figure 2 As shown, a kind of 3D painting intelligent recommendation method based on deep learning and migration learning of the present invention comprises the following steps:

[0027] S1: Construct an image classifier based on a public image dataset, named RCLF; among them, the public image dataset, named BS is the MIT Computing Science Places205 public dataset, select the convolutional model Inception-ResNet to train on the Places205 public dataset Finally, obtain an image classifier RCLF that recognizes each scene in the Places205 public data set; the recognizer RCLF can classify 205 scenes in the Places205 public data set according to factors such as color, structure, and environment;

[0028] S2: 3D picture scene space transfer learning based on the image classifier RCLF obtained in step S1;

[0029] S21, keep the parameters of the image classifier RCLF except the softmax layer unchanged, increase the learning rate of the parameters of ...

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Abstract

The invention discloses a method for intelligent recommendation of a 3D picture based on deep learning and transfer learning. At first, a public big sample scene picture dataset is used to obtain a universal scene image classifier based on the deep learning; then, a 3D picture scene picture dataset collected by a user is used for the transfer learning, and the universal image classifier is converted into a 3D picture scene space classifier; then, a Hash perception algorithm is used to establish an information fingerprint base of a 3D picture design scheme recommendation picture base; and finally, a scene picture shot by the user is matched with the 3D picture design scheme base for screening, a matched candidate subset is obtained, an information fingerprint Hamming distance between each picture in the subset and the user's picture is computed, and the 3D picture with the minimum distance is recommended to the user. According to the invention, based on the deep learning and transfer learning, 3D picture design is achieved in a specific environment and a specific space structure; and design duration of the 3D picture is also shortened.

Description

technical field [0001] The invention relates to the field of machine learning and image processing, in particular to an intelligent recommendation method for 3D paintings based on deep learning and transfer learning. Background technique [0002] In recent years, naked-eye 3D paintings have received more and more attention and popularity due to their special artistic expression, super visual shock and interesting interactivity, covering many fields such as decoration, advertising, exhibitions and home furnishing. Has broad development prospects. 3D painting is a special art form that uses the principle of anti-perspective and optical illusion, and it needs to skillfully integrate the environment and space structure for creation. Therefore, it is time-consuming and labor-intensive to design a 3D painting according to a specific environment and spatial structure, and it also has certain requirements for the experience and level of the painter. In the traditional mode, a pain...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/583G06F18/2414
Inventor 王华珍潘傲寒
Owner HUAQIAO UNIVERSITY
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