Detection of Multiple Instances of Video Objects

This paper tackles the issue of retrieving different instances of an object of interest within a given video document or in a video database. The principle consists of considering a semi-global image representation based on an over-segmentation of image frames. An aggregation mechanism is then applied in order to group a set of sub-regions into an object similar to the query, under a global similarity criterion. Two different types of approaches are proposed. The first one involves a greedy, dynamic region construction method. The second is based on simulated annealing, and aims at determining a global optimum of the similarity function. Experimental results show promising performances, with FT and BE detection rates of up to 66% and 86%, respectively.