Painting style migration method based on saliency matching

A style of painting, a remarkable technology, applied in the intersection field, can solve problems such as whether the solid color area is located in an important object, etc.

Pending Publication Date: 2018-12-07
BEIJING UNIV OF TECH
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  • Application Information

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Problems solved by technology

Existing painting style migration methods tend to migrate the blank part to a solid color area i

Method used

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  • Painting style migration method based on saliency matching
  • Painting style migration method based on saliency matching
  • Painting style migration method based on saliency matching

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Embodiment Construction

[0018] Each module of the present invention, as well as the branch training strategy and the use method of the present invention will be further described below in conjunction with the drawings and specific embodiments.

[0019] Module composition and flow process of the present invention are as figure 1 As shown, it specifically includes the following modules:

[0020] Module 1, feature extraction.

[0021] The feature extraction module is similar to the module structure given in the "Perceptual Losses for Real-Time StyleTransfer and Super-Resolution" paper by Johnson et al., consisting of an encoder and a residual subnetwork. The encoder implements multi-scale feature extraction and nonlinear mapping calculation through an L-layer convolutional neural network to obtain feature expression at the semantic level, and then further condenses features through a J-layer residual module. The use of residual subnetwork can speed up the convergence speed of network training while sa...

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Abstract

The invention relates to a painting style migration method based on saliency matching. The method is characterized by providing a painting style migration depth neural network model based on saliencymatching; and the model is formed by four modules: feature extraction, sub painting style migration, saliency-based region decomposition, and painting style image synthesis. During training, branch training is carried out on the constructed painting style migration network model and thus each branch is optimized towards target effects. Compared with the prior art, the painting style migration method has the following advantages: firstly, painting style migration is carried out based on the consistency of object saliency in a content image and sub painting style saliency in a painting style image and thus the salient painting style can be migrated to the salient object in the content image, thereby ensuring unchanging of the object saliency sequence in the content image after style migration; and objects with different saliency carry on the single sub painting style, so that chaotic style mixing caused by carrying on different sub painting styles by the same object is avoided; and secondly, the generated image with the painting style becomes smooth without noises.

Description

technical field [0001] The invention belongs to the intersection fields of digital image processing, computer vision, art, etc., and relates to a painting style migration method based on saliency matching. Background technique [0002] In recent years, the rapid development of technology in the field of digital image processing and computer vision has driven the application of these technologies in other fields, and these applications have in turn prompted continuous technological innovation. The transfer of painting style is a subject that arises in such an environment. Style transfer is given a digital image of an artistic painting (called style map), converting photos taken by users or digital images made by other methods (collectively called content map) into the style of the artistic painting. The transfer of painting style can greatly reduce the threshold of artistic creation, allowing the general public to easily create images with artistic style. [0003] In recent...

Claims

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Application Information

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IPC IPC(8): G06T11/00G06K9/46G06K9/62
CPCG06T11/001G06V10/462G06F18/22G06F18/214
Inventor 孙安澜马伟祝玮
Owner BEIJING UNIV OF TECH
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