An application of pre-trained CNN for image classification

Abdullah, M. S. Hasan
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引用次数: 35

Abstract

Image Classification is a branch of computer vision where images are classified into categories. This is a very important topic in today's context as large databases of images are becoming very common. Images can be classified as supervised or unsupervised techniques. This paper investigates supervised classification and evaluates performances of two classifiers as well as two feature extraction techniques. The classifiers used are Linear Support Vector Machine (SVM) and Quadratic SVM. The classifiers are trained and tested with features extracted using Bag of Words and pre-trained Convolution Neural Network (CNN), namely AlexNet. It has been observed that the classifiers are able to classify images with very high accuracy when trained with features from CNN. The image categories consisted of Binocular, Motorbikes, Watches, Airplanes, and Faces, which are taken from Caltech 265 image archive.
预训练CNN在图像分类中的应用
图像分类是计算机视觉的一个分支,其中图像被分类成不同的类别。在今天的背景下,这是一个非常重要的话题,因为大型图像数据库变得非常普遍。图像可以分为监督技术和非监督技术。本文研究了监督分类,并对两种分类器和两种特征提取技术的性能进行了评价。使用的分类器是线性支持向量机(SVM)和二次支持向量机。分类器通过使用Words Bag和预训练卷积神经网络(CNN)即AlexNet提取的特征进行训练和测试。已经观察到,当使用CNN的特征进行训练时,分类器能够以非常高的准确率对图像进行分类。图像类别包括双目望远镜、摩托车、手表、飞机和人脸,它们取自加州理工学院265图像档案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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