Annotating personal albums via web mining

Nowadays personal albums are becoming more and more popular due to the explosive growth of digital image capturing devices. An effective automatic annotation system for personal albums is desired for both efficient browsing and search. Existing research on image annotation evolves through two stages: learning-based methods and web-based methods. Learning-based methods attempt to learn classifiers or joint probabilities between images and concepts, which are difficult to handle large-scale concept sets due to the lack of training data. Web-based methods leverage web image data to learn relevant annotations, which greatly expand the scale of concepts. However, they still suffer two problems: the query image lacks prior knowledge and the annotations are often noisy and incoherent. To address the above issues, we propose a web-based annotation approach to annotate a collection of photos simultaneously, instead of annotating them independently, by leveraging the abundant correlations among the photos. A multi-graph similarity propagation based semi-supervised learning (MGSP-SSL) algorithm is proposed to suppress the noises in the initial annotations from the Web. Experiments on real personal albums show that the proposed approach outperforms existing annotation methods.