Unsupervised GIST based Clustering for Object Localization

Abstract

In the past years, there have been several attempts for the task of object localization in an image. However, most of the algorithms for object localization have been either supervised or weakly supervised. The work presented in this paper is based on the localization of a single object instance, in an image, in a fully unsupervised manner. Initially, from the input image, object proposals are generated where the proposal score for each of these proposals is calculated using a saliency map. Next, a graph by the GIST feature similarity between each pair of proposals is constructed. Density-based spatial clustering of applications with noise (DBSCAN) is used to make clusters of proposals based on GIST similarity, which eventually helps us in the final localization of the object. The setup is evaluated on two challenging benchmark datasets - PASCAL VOC 2007 dataset and object discovery dataset. The performance of the proposed approach is observed to be comparable with various state-of-the-art weakly supervised and unsupervised approaches for the problem of localization of an object.