Generic image classification using visual knowledge on the web
Generic image classification using visual knowledge on the web
复制标题
使用网络上的视觉知识进行通用图像分类
DOI：
10.1145/957013.957047
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Keiji Yanai
中科院分区：
文献类型：
--
作者：
Keiji Yanai
在本文中，我们描述了一个通用图像分类系统，具有来自万维网的自动知识获取机制。由于近年来数字成像设备的普及，对各种现实世界场景的图像识别的需求变得更大。为了实现它，需要各种场景的视觉知识。然后，我们建议收集现实世界场景的视觉知识，以便从万维网进行通用图像分类。我们的系统自动从网络收集大量图像，并将它们用作通用图像分类的训练图像。它由三个模块组成，即图像采集模块、图像学习模块和图像分类模块。图像收集模块自动从网络上收集与给定类别关键字相关的图像。学习模块从收集的图像中提取图像特征并将它们与每个类别相关联。图像分类模块利用图像特征与类别之间的关联性将未知图像分类为与类别关键词对应的类别之一。在实验中，我们使用从万维网自动收集的图像作为训练图像，对通用图像实现了 44.6% 的分类率。
In this paper, we describe a generic image classification system with an automatic knowledge acquisition mechanism from the World-Wide Web. Due to the recent spread of digital imaging devices, the demand for image recognition of various kinds of real world scenes becomes greater. For realizing it, visual knowledge on various kinds of scenes is required. Then, we propose gathering visual knowledge on real world scenes for generic image classification from the World-Wide Web. Our system gathers a large number of images from the Web automatically and makes use of them as training images for generic image classification. It consists of three modules, which are an image-gathering module, an image-learning module and an image classification module. The image-gathering module gathers images related to given class keywords from the Web automatically. The learning module extracts image features from gathered images and associates them with each class. The image classification module classifies an unknown image into one of the classes corresponding to the class keywords by using the association between image features and classes. In the experiments, we achieved a classification rate 44.6% for generic images by using images gathered from the World-Wide Web automatically as training images.
影响因子：
19.5
作者：
Rubner, Y;Tomasi, C;Guibas, LJ
通讯作者：
Guibas, LJ