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We propose a novel image database categorization approach using a possibilistic clustering algorithm. The proposed algorithm is based on a robust data modeling using the Generalized Dirichlet (GD) finite mixture and generates two types of membership degrees. The first one is a posterior probability that indicates the degree to which the point fits the estimated distribution. The second membership...
We present a method for fusing the decisions of multiple algorithms that use different hyperspectral imagery (HI) classification methods and apply it to mine detection. The proposed fusion method, called Cumulative Separation-Based (CSB) method, is embedded into our Context-Dependent Fusion for Multiple Algorithms(CDF-MA) framework. The CDF-MA is motivated by the fact that the relative performance...
In this paper, we propose a generic approach for representing image texture features in a compact and intuitive way. Our approach, called Dominant Texture Descriptor (DTD), is inspired by the dominant color descriptor. It is based on clustering the local texture features and identifying the dominant components and their spatial distribution. We also present an enhanced version of the DTD (eDTD) that...
We propose an adaptive constrained clustering (ACC) algorithm that performs clustering and feature weighting simultaneously and that can incorporate partial supervision information. This information consists of a set of constraints on which instances should or should not reside in the same cluster. The algorithm is dynamic in the sense that the optimal number of clusters can expand or shrink depending...
In this paper, we address the problem of transforming relational features into an Euclidian space so that standard classification methods that assume that data is in a vector form could be used. Our approach has three main steps. First, a relational matrix that represents the pair-wise dissimilarities between all objects is constructed. Second, a fuzzy relational clustering algorithm is used to partition...
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