Semantic Annotation of View-based Object Representations Using Online Search Engines (eBook)

eBook Download: PDF

2023 | 1. Auflage
GRIN Verlag
978-3-346-97933-9 (ISBN)

Infos

Master's Thesis from the year 2015 in the subject Computer Science - Internet, New Technologies, grade: 1.3, University of Paderborn (Institute of Electrical Engineering and Information Technology), language: English, abstract: In this master’s thesis, an automatic image annotation algorithm for object recognition systems is being developed and evaluated. Addressing the semantic gap problem, it is investigated by which means images of objects can automatically be labelled with textual tags that represent corresponding semantic information. A central question in this context is to what extent online databases are exploitable for such an annotation algorithm. Representative works from the literature normally use large scale databases containing images along their features and their semantic information. Queued images can be annotated by searching the database for images with similar features and using the provided semantics.

This work focuses another approach, where the semantic information are extracted from additional sources, such as texts according to visually similar images on websites. Those images are found in online databases or on the web using search engines. By this means, a broad range of objects can be handled without the need to create a database. Techniques will be implemented and analyzed that find websites containing images similar to the image to be annotated and that mine semantic information from corresponding texts. Eventually the system will be tested with a range of images with objects of different difficulties.

Produkt-Details

• Erscheint lt. Verlag: 5.12.2023
• Verlagsort: München
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Web / Internet
• Schlagworte: Annotation • Engines • object • Online • representations • Search • Semantic • Using • view-based
• ISBN-10: 3-346-97933-4 / 3346979334
• ISBN-13: 978-3-346-97933-9 / 9783346979339