Combining labeled and unlabeled data with co-training
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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.