Feature selection under learning to rank model for multimedia retrieve

Most multimedia retrieval problem can be described by ranking model, i.e. the images in the database could be ranked according to the similarity compared with the query image. Existing ranking models generally use the features that are pre-defined by experts. This paper utilized machine learning techniques to automatically select useful features for ranking. We first generate a set of feature subsets by putting each feature into an individual feature subset. Then we sort these feature subsets according to the ranking performances. Third, two neighbor feature subsets in the ranked order are pairwised to generate a new feature subset. The new feature subsets are sorted based on the new ranking performance. Iterate until reach the pre-defined stop point. Experimental results on .gov dataset and Caltech101 development set show the effectiveness and efficiency of the proposed algorithm.