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Hybridized Cuckoo Search with Multi-Verse Optimization-Based Patch Matching and Deep Learning Concept for Enhancing Video Inpainting
The Computer Journal ( IF 1.4 ) Pub Date : 2021-05-06 , DOI: 10.1093/comjnl/bxab067
B Janardhana Rao 1 , Y Chakrapani 2 , S Srinivas Kumar 3
Affiliation  

This paper aims to develop a novel deep learning concept to deal with video inpainting. Initially, motion tracking is performed, which is the process of determining motion vectors that describe the transformation from adjacent frames in a video sequence. Further, the regions or patches of each frame are categorized using the optimized recurrent neural network (RNN), in which the region is split into a smooth and structure region. It is performed using the texture feature called grey-level co-occurrence matrix. The filling of the smooth region is accomplished by replacing with the mean pixels of unmasked region, and the structure region is done by optimized patch matching approach based on scale-invariant feature transform (SIFT). The main objective optimized patch matching is based on the minimized Euclidean distance between the extracted SIFT features of the original patch and reference patch, and the certain patch is inpainted by the optimized patch. Here, the hybridization of two meta-heuristic algorithms like cuckoo search algorithm and multi-verse optimization (MVO) called Cuckoo Search-based MVO is used to optimize the RNN and patch matching. Finally, the experimental results verify the reliability of the proposed algorithm over existing algorithms.

中文翻译:

混合杜鹃搜索与基于多节优化的补丁匹配和用于增强视频修复的深度学习概念

本文旨在开发一种新颖的深度学习概念来处理视频修复。最初,执行运动跟踪,这是确定描述视频序列中相邻帧的变换的运动矢量的过程。此外,使用优化的循环神经网络 (RNN) 对每一帧的区域或补丁进行分类,其中区域被分割为平滑和结构化的区域。它使用称为灰度共生矩阵的纹理特征来执行。平滑区域的填充是通过替换未掩蔽区域的平均像素来完成的,结构区域是通过基于尺度不变特征变换(SIFT)的优化补丁匹配方法完成的。优化patch匹配的主要目标是根据提取的原始patch和参考patch的SIFT特征之间的欧几里得距离最小化,并用优化后的patch修复某个patch。在这里,两种元启发式算法的混合,如布谷鸟搜索算法和多节优化 (MVO),称为基于布谷鸟搜索的 MVO,用于优化 RNN 和补丁匹配。最后,实验结果验证了所提算法相对于现有算法的可靠性。
更新日期:2021-05-06
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