一种SVM学习框架下的Web3D轻量级模型检索算法

Web3D Lightweight for Sketch-Based Shape Retrieval Using SVM Learning Algorithm

随着Web3D技术的发展,对于互联网检索三维模型的需求越来越迫切,特别是基于草图的模型检索.本文对基于草图的三维模型检索相关技术进行了研究,提出了三维模型轻量化处理算法、基于支持向量机三维模型最佳视点选择算法.本文首先对模型进行简化处理,投影三维模型为多个视点图像.其次,使用支持向量机在草图数据集上学习规则,并根据规则进行相应的视点图像分类,获得最佳视点图像.再次,对视点图像提取梯度直方图特征并进行K-means聚类和索引,减少特征空间,获得三维模型的特征字典.最后,在开源数据集上进行相关的实验并对结果进行分析,相关结果表明方法具有很强鲁棒性、准确性.

With the rapid development of Web3D technology,the demand to use and retrieve 3D model is becoming urgent.Especially,sketch-based shape retrieval is very important.In the paper,a framework is proposed,which includes lightweight for shape,SVM-based learning algorithm.In particular,the model is simplified and then projected into multi-views images.Besides,a SVM classifier is used to classify these images.Moreover,histogram of oriented gradient (HOG) features is extracted from the input sketch image.Furthermore,K-means algorithm is used to cluster and index these features in order to generate a features dictionary.Finally,the related experiments are conducted to verify the feasibility of the approach in open source datasets.The result shows that the proposed method is robust and superior,compared with other methods.