AKULA -- Adaptive Cluster Aggregation for Visual Search

Key point features are very effective tools in image matching and key point feature aggregation is an effective scheme for creating a compact representation of the images for visual search. This solution not only achieves compression, but also offers the benefits of better accuracy in matching and indexing efficiency. Research is active in this area and recent results on Fisher Vector based aggregation have shown to be very effective in a number of application scenarios. In this paper, we present a new direct aggregation scheme that is adaptive to the descriptor distributions from individual images and does not enforce a single generative model such as GMM in the Fisher Vector type aggregation. Moreover, it achieves better compression as well as image matching accuracy. Simulation results with the image identification data set from MPEG Compact Descriptor for Visual Search (CDVS) effort demonstrate the effectiveness of this approach.