A probabilistic framework for unsupervised evaluation and ranking of image segmentations

In this paper, a Bayesian Network (BN) framework for unsupervised evaluation of image segmentation quality is proposed. This image understanding algorithm utilizes a set of given Segmentation Maps (SMs) ranging from under-segmented to over-segmented results for a target image, to identify the semantically meaningful ones and rank the SMs according to their applicability in image processing and computer vision systems. Images acquired from the Berkeley segmentation dataset along with their corresponding SMs are used to train and test the proposed algorithm. Low-level local and global image features are employed to define an optimal BN structure and to estimate the inference between its nodes. Furthermore, given several SMs of a test image, the optimal BN is utilized to estimate the probability that a given map is the most favorable segmentation for that image. The algorithm is evaluated on a separate set of images (none of which are included in the training set) wherein the ranked SMs (according to their probabilities of being acceptable segmentation as estimated by the proposed algorithm) are compared to the ground-truth maps generated by human observers. The Normalized Probabilistic Rand (NPR) index is used as an objective metric to quantify our algorithm's performance. The proposed algorithm is designed to serve as a pre-processing module in various bottom-up image processing frameworks such as content-based image retrieval and region-of-interest detection.