Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design Wise: A Guide for Evaluating the Interface Design of Information Resources
Design Wise: A Guide for Evaluating the Interface Design of Information Resources
Metadata Practices for Consumer Photos
IEEE MultiMedia
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Landmark detection from mobile life log using a modular Bayesian network model
Expert Systems with Applications: An International Journal
Automatic feature selection for context recognition in mobile devices
Pervasive and Mobile Computing
Automatic tagging and geotagging in video collections and communities
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
A review on automatic image annotation techniques
Pattern Recognition
Automatic image tagging based on regions of interest
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Using activity theory to model context awareness
MRC'05 Proceedings of the Second international conference on Modeling and Retrieval of Context
Modeling Human Judgment of Digital Imagery for Multimedia Retrieval
IEEE Transactions on Multimedia
Semi-Automatic Tagging of Photo Albums via Exemplar Selection and Tag Inference
IEEE Transactions on Multimedia
A mobile picture tagging system using tree-structured layered Bayesian networks
Mobile Information Systems
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As digital media technologies have improved, a large amount of media content has been produced. Tagging is an effective way to manage a great volume of multimedia content. However, manual tagging has limitations such as human fatigue and subjective and ambiguous keywords. In this paper, we present an automatic tagging method to generate semantic annotation on a mobile phone. In order to overcome the constraints of the mobile environment, the method uses two layered Bayesian networks. In contrast to existing techniques, this approach attempts to design probabilistic models with fixed tree structures and intermediate nodes. To evaluate the performance of this method, an experiment is conducted with data collected over a month. The result shows the effectiveness of our proposed method. Furthermore, a simple graphic user interface is developed to visualize and evaluate recognized activities and probabilities.