Online Kernel Learning for interactive retrieval in dynamic image databases

In this paper, we propose a system for interactive image retrieval in dynamic databases, where images are regularly added or removed. In order to handle this, we propose a method that tunes itself according to user labels. The framework we propose is based on visual dictionaries, with the specificity that the dictionaries are built online, during retrieval sessions. In other words, each user has its own visual dictionary, as opposed to usual approaches where all users share the same visual dictionary. In order to create theses dictionaries, we propose a method based on kernel functions. This method iteratively selects base kernels from a large base kernel pool, where each base kernel is related to a low-level descriptor such as color or texture. This learning process is performed in real time, and the classification of the database is faster than usual techniques since only relevant features for the current query are used. Experiments are carried out on a generalist database, and show the ability of the method to build effective kernels with few labels.