TY - THES A1 - Bruns, Erich T1 - Adaptive Image Classification on Mobile Phones N2 - The advent of high-performance mobile phones has opened up the opportunity to develop new context-aware applications for everyday life. In particular, applications for context-aware information retrieval in conjunction with image-based object recognition have become a focal area of recent research. In this thesis we introduce an adaptive mobile museum guidance system that allows visitors in a museum to identify exhibits by taking a picture with their mobile phone. Besides approaches to object recognition, we present different adaptation techniques that improve classification performance. After providing a comprehensive background of context-aware mobile information systems in general, we present an on-device object recognition algorithm and show how its classification performance can be improved by capturing multiple images of a single exhibit. To accomplish this, we combine the classification results of the individual pictures and consider the perspective relations among the retrieved database images. In order to identify multiple exhibits in pictures we present an approach that uses the spatial relationships among the objects in images. They make it possible to infer and validate the locations of undetected objects relative to the detected ones and additionally improve classification performance. To cope with environmental influences, we introduce an adaptation technique that establishes ad-hoc wireless networks among the visitors’ mobile devices to exchange classification data. This ensures constant classification rates under varying illumination levels and changing object placement. Finally, in addition to localization using RF-technology, we present an adaptation technique that uses user-generated spatio-temporal pathway data for person movement prediction. Based on the history of previously visited exhibits, the algorithm determines possible future locations and incorporates these predictions into the object classification process. This increases classification performance and offers benefits comparable to traditional localization approaches but without the need for additional hardware. Through multiple field studies and laboratory experiments we demonstrate the benefits of each approach and show how they influence the overall classification rate. N2 - Die Einführung von Mobiltelefonen mit eingebauten Sensoren wie Kameras, GPS oder Beschleunigungssensoren, sowie Kommunikationstechniken wie Bluetooth oder WLAN ermöglicht die Entwicklung neuer kontextsensitiver Anwendungen für das tägliche Leben. Insbesondere Applikationen im Bereich kontextsensitiver Informationsbeschaffung in Verbindung mit bildbasierter Objekterkennung sind in den Fokus der aktuellen Forschung geraten. Der Beitrag dieser Arbeit ist die Entwicklung eines bildbasierten, mobilen Museumsführersystems, welches unterschiedliche Adaptionstechniken verwendet, um die Objekterkennung zu verbessern. Es wird gezeigt, wie Ojekterkennungsalgorithmen auf Mobiltelefonen realisiert werden können und wie die Erkennungsrate verbessert wird, indem man zum Beispiel ad-hoc Netzwerke einsetzt oder Bewegungsvorhersagen von Personen berücksichtigt. T2 - Adaptive Bilderkennung auf Mobiltelefonen KW - Kontextbezogenes System KW - Bilderkennung KW - Ubiquitous Computing KW - Mobile Computing KW - Maschinelles Sehen KW - Museumsführer KW - Handy KW - Wegrouten KW - Positionsbestimmung KW - PhoneGuide KW - Bluetooth tracking KW - pathway awareness KW - museum guidance system KW - PhoneGuide Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20100707-15092 ER - TY - JOUR A1 - Bruns, Erich A1 - Bimber, Oliver T1 - Phone-to-Phone Communication for Adaptive Image Classification N2 - In this paper, we present a novel technique for adapting local image classifiers that are applied for object recognition on mobile phones through ad-hoc network communication between the devices. By continuously accumulating and exchanging collected user feedback among devices that are located within signal range, we show that our approach improves the overall classification rate and adapts to dynamic changes quickly. This technique is applied in the context of PhoneGuide – a mobile phone based museum guidance framework that combines pervasive tracking and local object recognition for identifying a large number of objects in uncontrolled museum environments. KW - Peer-to-Peer-Netz KW - Bilderkennung KW - Museumsführer KW - Ad-hoc-Netz KW - Phone-to-phone communication KW - adaptive image classification KW - mobile ad-hoc networks KW - museum guidance system Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20080722-13685 ER -