基于SVM和局部描述符的野生动物分类

Slavomir Matuska, Robert Hudec, Patrik Kamencay, Miroslav Benco, Martina Zachariasova
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引用次数: 23

摘要

提出了一种基于混合局部描述子的目标识别方法。该方法结合SIFT -尺度不变特征变换和SURF -加速鲁棒特征变换两种方法,分为第二部分。在数据集中的少量图像上验证了混合方法的适用性。数据集类代表位于斯洛伐克国家的大型动物,即狼、狐狸、棕熊、鹿和野猪。该方法也可用于图像分类和特征提取的其他领域。实验结果表明,局部描述符的组合对目标识别有积极的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Wild Animals based on SVM and Local Descriptors

In this paper, a novel method for object recognition based on hybrid local descriptors is presented. This method utilizes a combination of a few approaches (SIFT - Scale-invariant feature transform, SURF - Speeded Up Robust Features) and consists of second parts. The applicability of the presented hybrid methods are demonstrated on a few images from dataset. Dataset classes represent big animals situated in Slovak country, namely wolf, fox, brown bear, deer and wild boar. The presented method may be also used in other areas of image classification and feature extraction. The experimental results show, that the combination of local descriptors has a positive effect for object recognition.

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