Conference Paper

Efficient Binary Fuzzy Measure Representation and Choquet Integral Learning

Abstract The Choquet integral (ChI), a parametric function for information aggregation, is parameterized by the fuzzy measure (FM), which has 2^N real-valued variables for N inputs. However, the ChI incurs huge storage and computational burden due to its exponential complexity relative to N and, as a result, its calculation, storage, and learning becomes intractable for …

Environmentally-Adaptive Target Recognition for SAS Imagery

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Abstract Characteristics of underwater targets displayed in synthetic aperture sonar (SAS) imagery vary depending on their environmental context. Discriminative features in sea grass may differ from the features that are discriminative in sand ripple, for example. Environmentally-adaptive target detection and classification systems that take into account environmental context, therefore, have the potential for improved results. …

Multiple-instance Learning-based Sonar Image Classification

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Abstract An approach to image labeling by seabed context based on multiple-instance learning via embedded instance selection (MILES) is presented. Sonar images are first segmented into superpixels with associated intensity and texture feature distributions. These superpixels are defined as the “instances” and the sonar images are defined as the “bags” within the MILES classification framework. …

Multiple Instance Choquet Integral for Classifier Fusion

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Abstract The Multiple Instance Choquet integral (MICI) for classifier fusion and an evolutionary algorithm for parameter estimation is presented. The Choquet integral has a long history of providing an effective framework for non-linear fusion. Previous methods to learn an appropriate measure for the Choquet integral assumed accurate and precise training labels (with low levels of …

Possibilistic context identification for SAS imagery

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Abstract This paper proposes a possibilistic context identification approach for synthetic aperture sonar (SAS) imagery. SAS seabed imagery can display a variety of textures that can be used to identify seabed types such as sea grass, sand ripple and hard-packed sand, etc. Target objects in SAS imagery often have varying characteristics and features due to …