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首页> 外文期刊>Journal of ambient intelligence and humanized computing >Automated Hand-drawn sketches retrieval and recognition using regularized Particle Swarm Optimization based deep convolutional neural network
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Automated Hand-drawn sketches retrieval and recognition using regularized Particle Swarm Optimization based deep convolutional neural network

机译：自动化手绘草图检索和识别使用正则化粒子群优化基于深度卷积神经网络

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摘要

One of the most popular and rising research area of image processing is free hand-drawn sketch recognition and its retrieval. Enlarger number of methods is introduced to retrieve the sketch images but it made few complexity issues and their performance often degraded. So, in this paper, we proposed an effective method of Regularized Particle Swarm Optimization Based Deep Convolutional Neural Network (RPSO-DCNN) algorithm to retrieve the performance of free hand-drawn sketches. In feature extraction, the Regularized Particle Swarm Optimization (RPSO) model that aim is to produce an optimal evolutionary deep learning result. Therefore, the free hand-drawn sketch image classification and its retrieval are performed by Support Vector Machine and Levenshtein distance-based fuzzy k-nearest neighbour (L-FkNN) algorithms. Hence, this work can bring in communication between human and computer. Experimentally, the simulation work of the proposed RPSO-DCNN model is implemented in the running software of MATLAB. The sketch images are chosen from the TU-Berlin dataset, Sketch dataset, SHREC13 dataset, Flickr dataset and Sketchy dataset. Aiming is to facilitate the performance of the proposed RPSO-DCNN model with various kinds of state of art methods such as H-CNN, Fuzzy, CNN, MARQS and TCVD. The experimental result demonstrates that, the proposed RPSO-DCNN accomplish the optimal accuracy with different state-of-art methods.
机译：图像处理最受欢迎和上升的研究领域之一是免费手绘草图识别及其检索。引入放大的方法数量来检索草图图像，但它缺少了一些复杂性问题，并且它们的性能通常会降级。因此，在本文中，我们提出了一种有效的基于深度卷积神经网络（RPSO-DCNN）算法的正则化粒子群优化方法，以检索自由手绘草图的性能。在特征提取中，旨在产生最佳进化深层学习结果的正则化粒子群优化（RPSO）模型。因此，通过支持向量机和基于Levenshtein距离的模糊K最近邻（L-FKNN）算法来执行自由的手绘草图图像分类及其检索。因此，这项工作可以带来人与计算机之间的沟通。实验，所提出的RPSO-DCNN模型的仿真工作是在MATLAB的运行软件中实现的。草图图像选自TU-Berlin数据集，草图数据集，SHREC13数据集，Flickr DataSet和Scressy DataSet。目的是促进所提出的RPSO-DCNN模型的性能，具有各种现有技术方法，如H-CNN，模糊，CNN，MARQS和TCVD。实验结果表明，所提出的RPSO-DCNN采用不同的最先进方法实现最佳精度。

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