Audiovisual video context recognition using SVM and genetic algorithm fusion rule weighting

Roininen, Mikko J.; Guldogan, Esin; Gabbouj, Moncef

doi:10.1109/cbmi.2011.5972541

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A lot of research has already been focusing on the detection and representation of and the reasoning about the context or activity of users in a mobile or smart (home) environment. For instance, within the domain of activity detection, solutions have been proposing the usage of for example video data [21], different types of sensor data, such as door sensors and light switches [7], wireless sensors [25], RFID tags [20], accelerometer data [10], etc. In addition, several mobile context-aware service platforms have been designed in order to simplify the development of context-aware mobile applications [19,5,14,6].…”

Section: Related Workmentioning

confidence: 99%

Mobile application usage prediction through context-based learning

Leroux

Roobroeck

Dhoedt

et al. 2013

Journal of Ambient Intelligence and Smart Environments

View full text Add to dashboard Cite

The purchase and download of new applications on all types of smartphones and tablet computers has become increasingly popular. On each mobile device, many applications are installed, often resulting in crowded icon-based interfaces. In this paper, we present a framework for the prediction of a user's future mobile application usage behavior. On the mobile device, the framework continuously monitors the user's previous use of applications together with several context parameters such as speed and location. Based on the retrieved information, the framework automatically deduces application usage patterns. These patterns define a correlation between a used application and the monitored context information or between different applications. Furthermore, by combining several context parameters, context profiles are automatically generated. These profiles typically match with real life situations such as 'at home' or 'on the train' and are used to delimit the number of possible patterns, increasing both the positive prediction rate and the scalability of the system. A concept demonstrator for Android OS was developed and the implemented algorithms were evaluated in a detailed simulation setup. It is shown that the developed algorithms perform very well with a true positive rate of up to 90% for the considered evaluation scenarios.

show abstract