Mean shift based feature space analysis has been shown to be an elegant, accurate and robust technique. The elegance in this non-parametric algorithm is mainly due to its simplicity in performing gradient ascent to estimate the modes in a multidimensional data. One characteristic aspect of mean shift is that the mode estimation is performed at each data point. Since it is important to describe the data in as succinct manner as possible, it is important to focus on modal points in the data instead of every data point. In this paper, we attempt to tackle the mean shift problem through a "mode centric" approach using swarm intelligence. Here, the mode estimation is cast as a problem of goal seeking for the swarm as it moves through the multidimensional data space. Local maxima/minima and plateaus are avoided through information exchange between each member of the swarm, thereby converging at the mode values efficiently.