Image segmentation by fusion of low level and domain specific information via Markov Random Fields

Abstract

We propose a new segmentation method by fusing a set of top-down and bottom-up segmentation maps under the Markov Random Fields (MRF) framework. The bottom-up segmentation maps are obtained by varying the parameters of an unsupervised segmentation method, such as Mean Shift. The top-down segmentation maps are constructed from some priori information, called domain specific information (DSI), received from a domain expert in the form of general properties about the image dataset. The properties are then used to generate domain specific maps (DSM) using logical predicates. Information gathered from the fusion of the bottom-up segmentation maps together with the domain specific maps are utilized to determine the pairwise potentials in the energy function of an unsupervised MRF model. Due to the inclusion of domain specific information, this approach can be considered as a first step to semantic image segmentation under an unsupervised MRF model. The proposed system is compared with the state of the art unsupervised image segmentation methods and satisfactory results are observed.