Measuring the Local Non-alignment Between Objects: Applications to Different Domains

详细信息    查看全文

• 作者：Radu Tudor Ionescu ^{raducu.ionescu@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：local non-alignment ; distance measure ; similarity measure ; kernel methods ; similarity-based learning ; optical character recognition ; object recognition ; native language identification
• 刊名：Procedia Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：96
• 期：Complete
• 页码：838-847
• 全文大小：3076 K

文摘

At a first glance, computer vision and text mining may seem to be unrelated fields of study, but image analysis and text or string processing are in many ways similar. As will be shown in this paper, the concept of treating image and text in a similar fashion has proven to be very fertile for specific applications in computer vision and text mining. By adapting text and string processing techniques to image processing or the other way around, knowledge from one domain can be transferred to the other. In fact, many breakthrough discoveries have been made by transferring knowledge between different domains. This work is centered around the idea of measuring the local non-alignment among two objects and use it as a similarity or distance function between the respective objects. Remarkably, this idea shows its uses in different domains. More precisely, the local non-alignment can be computed between two images, two text documents, or even two DNA sequences. As such, a variety of applications are exhibited in this paper, ranging from optical character recognition and object recognition to native language identification.