Category: Publication

Abstract: In hyperspectral unmixing applications, one typically assumes that a single spectrum exists for every endmember. In many scenarios, this is not the case, and one requires a set or a distribution of spectra to represent an endmember or class. This inherent spectral variability can pose severe difficulties in classical unmixing approaches. In this paper, […]

Abstract: A multiple instance learning (MIL) method, extended Function of Multiple Instances (eFUMI), is applied to ballistocardiogram (BCG) signals produced by a hydraulic bed sensor. The goal of this approach is to learn a personalized heartbeat ”concept” for an individual. This heartbeat concept is a prototype (or ”signature”) that characterizes the heartbeat pattern for an […]

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 […]

Abstract: Several techniques exist for dealing with spectral variability in hyperspectral unmixing, such as multiple endmember spectral mixture analysis (MESMA) or compositional models. These algorithms are computationally very involved, and often cannot be executed on problems of reasonable size. In this work, we present a new algorithm for solving the unmixing problem when spectral variability […]

Abstract: For many years, topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting and recognizing objects in imagery simultaneously. However, these models are confined to the analysis of categorical data, forcing a visual word to belong to one and only one topic. There are many images in which some regions cannot be […]

Abstract: The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a whitened coordinate space. The space is whitened according to an estimation of the background statistics, which directly effects the performance of the statistic as a target detector. In this paper, the […]

Abstract: Many effective supervised discriminative dictionary learning methods have been developed in the literature. However, when training these algorithms, precise ground-truth of the training data is required to provide very accurate point-wise labels. Yet, in many applications, accurate labels are not always feasible. This is especially true in the case of buried object detection in […]

Abstract: Handheld ground penetrating radar (GPR) enables the detection of subsurface objects under different terrains or over regions with significant amount of metal debris. The challenge for the handheld GPR is to reduce the false alarm rate and limit the undesirable human operator effect. This paper proposes the use of log-Gabor features to improve the […]

Abstract: The Extended Functions of Multiple Instances (eFUMI) algorithm is a generalization of Multiple Instance Learning (MIL). In eFUMI, only bag level (i.e. set level) labels are needed to estimate target signatures from mixed data. The training bags in eFUMI are labeled positive if any data point in a bag contains or represents any proportion […]

Abstract: Dictionary learning techniques have proven to be a powerful method in the pattern recognition literature. Recently supervised dictionary learning has been used to achieve very good results on a number of different data types and applications. However, these supervised dictionary learning algorithms do not perform as well when the data contains a number of […]