Active learning for tag recommendation utilizing on-line photos lacking tags

摘要

Recommending text tags for on-line photos is useful for Internet photo services. Typical solutions to this problem require analysis of the correlation among different attributes of the photos, including the correlation between the textual features and visual features computed from a photo. However, most on-line photos have very few tags or even no tags, and thus they contribute little or none to the analysis of tag-photo correlation, which is a key component in those schemes that rely on such analysis for tag recommendation. To address this practical challenge, we propose an active learning method for incorporating photos with no or few tags so as to enhance the correlation analysis for improved performance in tag recommendation. We demonstrate the effectiveness of the proposed approach using a dataset of more than 33,000 photos collected from Flickr.