Abstract
In this paper we address the problem of location recognition from visual lifelogs by leveraging visual features and temporal information in an unified framework. The proposed method features a co-training approach that takes advantage of both labeled and unlabeled data using a confidence measure we propose for this task. It exploits jointly two SVM classifiers on two types of visual features as well as the temporal continuity of the video through temporal accumulation scheme. We demonstrate experimentally on the publicly available IDOL2 dataset that the algorithm yields performance improvement due to its ability to exploit jointly multiple cues, time and unlabeled data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
