Clustering in 2D space can be adapted as a segmentation method in images. In this study, we improve one well-known clustering algorithm, DBSCAN, to tackle pattern recognition problems in natural images. In DBSCAN, the details of objects are lost because of the noise in the scene or boundary regions. We overcome this problem using multi-scale approach to collect the salient features at different scales for better clustering. We use Gaussian kernel to smooth an image since multi-scale approaches are shown to be a well modeled with this kernel. Comparing with manually segmented images as gold standard, we show that the proposed multi-scale framework outperforms the segmentation of objects obtained with DBSCAN.