Topic models [e.g., probabilistic latent semantic analysis, latent Dirichlet allocation (LDA), and supervised LDA] have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics. To address this, we present a partial membership LDA (PM-LDA) model and an associated parameter estimation algorithm. This model can be useful for imagery, where a visual word may be a mixture of multiple topics. Experimental results on visual and sonar imagery show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability previous topic modeling methods do not have.

Dictionary.com defines learning as the process of acquiring knowledge. In psychology, learning is defined as the modification of behavior through training. In our work, we combine these definitions to define learning as the modification of a system model to incorporate the knowledge acquired by new observations. During learning, the system creates and modifies a model to improve its performance. As new samples are introduced, the system updates its model based on the new information provided by the samples. However, this update may not necessarily improve the model. We propose a Bayesian surprise metric to differentiate good data (beneficial) from outliers (detrimental), and thus help to selectively adapt the model parameters. The surprise metric is calculated based on the difference between the prior and the posterior distributions of the model when a new sample is introduced. The metric is useful not only to identify outlier data, but also to differentiate between the data carrying useful information for improving the model and those carrying no new information (redundant). Allowing only the relevant data to update the model would speed up the learning process and prevent the system from overfitting. The method is demonstrated in all three learning procedures: supervised, semi-supervised and unsupervised. The results show the benefit of surprise in both clustering and outlier detection.

High-resolution synthetic aperture sonar (SAS) systems yield richly detailed images of seabed environments. Algorithms that automatically segment and label seabed textures such as coral, sea grass, sand ripple, and mud, require suitable features that discriminate between the texture classes. Here we present a robust, parameterized SAS image texture model based on the autocorrelation function (ACF) of the intensity image. This ACF texture model has been shown to accurately model first- and second-order statistical features of various seabed environments. An unsupervised multi-class k-means segmentation algorithm that uses the features derived from the ACF model is employed to label rock and ripple textures from a set of textured SAS images. The results of the segmentation are compared against the performance of the segmentation approach using biorthogonal wavelets and Haralick features. In the described experiments, the ACF model features are shown to produce better segmentations than the features based on wavelet coefficients and Haralick features for classifiers of low complexity.

The dynamic tree (DT) graphical model is a popular analytical framework for image segmentation and object classification tasks. A DT is a useful model in this context because its hierarchical property encodes information in multiple scales and its flexible structure fits complex region boundaries better than rigid quadtree structures such as tree-structured Bayesian networks. This paper proposes a novel framework for data fusion by using a DT model to fuse measurements from multiple sensing platforms into a non-redundant representation. The structural flexibility of the DT will be used to combine common information across different sensor measurements of simulated objects of interest. The appropriate structure of the DT and its parameters for the data fusion application are presented and discussed along with fusion results from a simulated sonar survey mission.