Scene matching using F-histogram-based features with possibilistic C-means optimization

This paper outlines further advances from initial findings previously reported in [O. Sjahputera, J.M. Keller, Possibilistic C-means in scene matching, Fourth Internat. Conf. of the European Society for Fuzzy Logic and Technology (EUSFLAT), 2005, pp. 669–675]. We propose a scene matching approach based on spatial relationships among objects in the images to determine if two images acquired under different viewing conditions capture the same scene. This is a difficult problem in computer vision. Our approach produces a mapping of objects from one view to the other, and recovers the viewing transformation parameters. The core of the system relies on capturing spatial relationship information through Force Histograms as affine-invariant image descriptors. Object mapping across images is performed by finding the best correspondence map (FMAP) between force histograms in the two images. The major problem is that the number of potential FMAPs is large, even for modest numbers of scene objects. Hence, search optimization is required. The correct FMAP contains histogram correspondences represented by similar feature vectors. Therefore, dense regions in the feature space are suspected to contain these vectors. Possibilistic C-means (PCM) clustering is used to find these dense regions. The centroids of these dense regions are used to generate the FMAPs. Previously, the FMAP was generated using a nearest-neighbor like approach. In this study, we propose an improved version of this method by incorporating fuzzy memberships into the FMAP building process. Here, the fitness of FMAP candidates are assessed with respect to all histogram correspondences already in FMAP, not just from an initial seed point alone. The best FMAP is selected and translated into a mapping scheme that connects the objects in the two images.