A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields

Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context, the present paper proposes an unsupervised and graph-based method of image segmentation, which is driven by an application goal, namely, the generation of image segments associated with a user-defined and application-specific goal. A graph, together with a random grid of source elements, is defined on top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven predicate and a suited parametric model for the data. In the experimental validation with both magnetic resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated product

An Automatic Data-Driven Method for SAR Image Segmentation in Sea Surface Analysis

\u2014 In the context of synthetic aperture radar (SAR) image segmentation, this paper proposes a new automatic unsupervised method addressing sea surface analysis with a focus on oil spill and ship segmentation. Being an evolution of an existing algorithm originally devoted to the detection of a single region of interest, the present method performs a global image segmentation of the whole image. The processing is independent of any model and is driven by data informative content along with intermediate results. Based on graph theory, it makes use of a new defined cost function and assigns cost values to the vertices rather than to the edges of the graph. The experimental results achieved and numerically evaluated on synthetic and real SAR images prove that the method is robust and repeatable, and it does not involve restrictions on image modality acquisition or sensors and does not require radiometric calibration. It can work on amplitude or intensity SAR images, independently on frequency band, polarimetry, and spatial resolution. Qualitative and quantitative performance analyses are carried out along with a comparison with other published works in the same application. Good results are achieved for both oil spill and ship segmentation and robustness by changing seed points position and number. Errors exhibit stable behavior when increasing the number of seed points. Finally, in contrast to most of the existing methods, the proposed technique does not depend on parameters and is generally more robust