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Mobile object retrieval in server-based image databases

: Manger, Daniel; Pagel, Frank; Widak, Heiko

Postprint urn:nbn:de:0011-n-2644587 (580 KByte PDF)
MD5 Fingerprint: b708af19f4357b82c66914fc24c60dbe
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 27.2.2014

Agaian, S.S. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Mobile multimedia/image processing, security, and applications 2013 : 29 - 30 April 2013, Baltimore, Maryland, United States
Bellingham, WA: SPIE, 2013 (Proceedings of SPIE 8755)
ISBN: 978-0-8194-9546-4
Paper 875515
Conference "Mobile Multimedia/Image Processing, Security, and Applications" <2013, Baltimore/Md.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.