We propose an adaptive constrained clustering (ACC) algorithm that performs clustering and feature weighting simultaneously and that can incorporate partial supervision information. This information consists of a set of constraints on which instances should or should not reside in the same cluster. The algorithm is dynamic in the sense that the optimal number of clusters can expand or shrink depending on the distribution of the data and the set of constraints. The ACC algorithm is used as the main tool to organize and navigate through a collection of geo-referenced images. In this application, the constraints are generated automatically based on the spatial distribution of the coordinates of the images and the dynamic range of the current view. As a result, images would be assigned to the same cluster if they are similar in content and close spatially. Icons of the clusterspsila representatives are superimposed on a map to provide the user with a global overview of the content and location of the photo collection. If the user selects a region to zoom-in, images within this region will be re-clustered with stricter spatial constraints. The proposed clustering and visualization application were applied to a collection of 2,023 photos taken with a digital camera that tags each photo with the location of where the photo was taken using a GPS receiver.
['Hichem Frigui', 'Jason Meredith']
Image database categorization under spatial constraints using adaptive constrained clustering