Label diagnosis through self tuning for web image search

Semi-supervised learning (SSL) relies on partial supervision information for prediction, where only a small set of samples are associated with labels. Performance of SSL is significantly degraded if the given labels are not reliable. Such problems arise in realistic applications such as web image search using noisy textual tags. This paper proposes a novel and efficient graph based SSL method with the unique capacity of pruning contradictory labels and inferring new labels through a bidirectional and alternating optimization process. The objective is to automatically identify the most suitable samples for manipulation, labeling or unlabeling, and meanwhile estimate a smooth classification function over a weighted graph. Different from other graph based SSL approaches, the proposed method employs a bivariate objective function and iteratively modifies label variables on both labeled and unlabeled samples. Starting from such a SSL setting, we present a relearning framework to improve the performance of base learner, particularly for the application of web image search. Besides the toy demonstration on artificial data, we evaluated the proposed method on Flickr image search with unreliable textual labels. Experimental results confirm the significant improvements of the method over the baseline text based search engine and the state-of-the-art SSL methods.