CNN based texture synthesize with Semantic segment

2016/05/16 by Xianye Liang, Bocheng Zhuo, Liang, Xianye +5
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Graphics (cs.GR) #Image Processing and 3D Reconstruction #Machine Learning (cs.LG) #cs.CV #cs.GR #cs.LG

paper · pdf · doi:10.48550/arxiv.1605.04731

7 pages, 4 figures. arXiv admin note: text overlap with arXiv:1505.07376, arXiv:1604.04339, arXiv:1602.07188 by other authors

arxiv created 2016/05/16 · openalex publication_date 2016/05/16 · arxiv updated 2016/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Deep learning algorithm display powerful ability in Computer Vision area, in recent year, the CNN has been applied to solve problems in the subarea of Image-generating, which has been widely applied in areas such as photo editing, image design, computer animation, real-time rendering for large scale of scenes and for visual effects in movies. However in the texture synthesize procedure. The state-of-art CNN can not capture the spatial location of texture in image, lead to significant distortion after texture synthesize, we propose a new way to generating-image by adding the semantic segment step with deep learning algorithm as Pre-Processing and analyze the outcome.

Citations

• OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• DRAW: A Recurrent Neural Network For Image Generation
• Gradient-based learning applied to document recognition
• ImageNet classification with deep convolutional neural networks
• Fully Convolutional Networks for Semantic Segmentation
• Learning Deconvolution Network for Semantic Segmentation
• Texture Synthesis Using Convolutional Neural Networks
• High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
• Exploring the Neural Algorithm of Artistic Style

Related

• Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints
• TextureGAN: Controlling Deep Image Synthesis with Texture Patches
• Image Inpainting for High-Resolution Textures using CNN Texture Synthesis
• Texture Synthesis Using Shallow Convolutional Networks with Random Filters
• Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection