SPOCC: Scalable POssibilistic Classifier Combination -- toward robust aggregation of classifiers

We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (possibly with overlap across learners) but each trained classifier can be evaluated on a validation dataset. We propose a new approach to aggregate the learner predictions in the possibility theory framework. For each classifier prediction, we build a possibility distribution assessing how likely the classifier prediction is correct using frequentist probabilities estimated on the validation set. The possibility distributions are aggregated using an adaptive t-norm that can accommodate dependency and poor accuracy of the classifier predictions. We prove that the proposed approach possesses a number of desirable classifier combination robustness properties

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Fuzzy Set Methods for Object Recognition in Space Applications

Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms

Image annotation and retrieval based on multi-modal feature clustering and similarity propagation.

The performance of content-based image retrieval systems has proved to be inherently constrained by the used low level features, and cannot give satisfactory results when the user\u27s high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. In this thesis we propose a system for image retrieval that has three mains components. The first component of our system consists of a novel possibilistic clustering and feature weighting algorithm based on robust modeling of the Generalized Dirichlet (GD) finite mixture. Robust estimation of the mixture model parameters is achieved by incorporating two complementary types of membership degrees. The first one is a posterior probability that indicates the degree to which a point fits the estimated distribution. The second membership represents the degree of typicality and is used to indentify and discard noise points. Robustness to noisy and irrelevant features is achieved by transforming the data to make the features independent and follow Beta distribution, and learning optimal relevance weight for each feature subset within each cluster. We extend our algorithm to find the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. We also outline a semi-supervised version of the proposed algorithm. In the second component of our system consists of a novel approach to unsupervised image annotation. Our approach is based on: (i) the proposed semi-supervised possibilistic clustering; (ii) a greedy selection and joining algorithm (GSJ); (iii) Bayes rule; and (iv) a probabilistic model that is based on possibilistic memebership degrees to annotate an image. The third component of the proposed system consists of an image retrieval framework based on multi-modal similarity propagation. The proposed framework is designed to deal with two data modalities: low-level visual features and high-level textual keywords generated by our proposed image annotation algorithm. The multi-modal similarity propagation system exploits the mutual reinforcement of relational data and results in a nonlinear combination of the different modalities. Specifically, it is used to learn the semantic similarities between images by leveraging the relationships between features from the different modalities. The proposed image annotation and retrieval approaches are implemented and tested with a standard benchmark dataset. We show the effectiveness of our clustering algorithm to handle high dimensional and noisy data. We compare our proposed image annotation approach to three state-of-the-art methods and demonstrate the effectiveness of the proposed image retrieval system

Speech data analysis for semantic indexing of video of simulated medical crises.

The Simulation for Pediatric Assessment, Resuscitation, and Communication (SPARC) group within the Department of Pediatrics at the University of Louisville, was established to enhance the care of children by using simulation based educational methodologies to improve patient safety and strengthen clinician-patient interactions. After each simulation session, the physician must manually review and annotate the recordings and then debrief the trainees. The physician responsible for the simulation has recorded 100s of videos, and is seeking solutions that can automate the process. This dissertation introduces our developed system for efficient segmentation and semantic indexing of videos of medical simulations using machine learning methods. It provides the physician with automated tools to review important sections of the simulation by identifying who spoke, when and what was his/her emotion. Only audio information is extracted and analyzed because the quality of the image recording is low and the visual environment is static for most parts. Our proposed system includes four main components: preprocessing, speaker segmentation, speaker identification, and emotion recognition. The preprocessing consists of first extracting the audio component from the video recording. Then, extracting various low-level audio features to detect and remove silence segments. We investigate and compare two different approaches for this task. The first one is threshold-based and the second one is classification-based. The second main component of the proposed system consists of detecting speaker changing points for the purpose of segmenting the audio stream. We propose two fusion methods for this task. The speaker identification and emotion recognition components of our system are designed to provide users the capability to browse the video and retrieve shots that identify ”who spoke, when, and the speaker’s emotion” for further analysis. For this component, we propose two feature representation methods that map audio segments of arbitary length to a feature vector with fixed dimensions. The first one is based on soft bag-of-word (BoW) feature representations. In particular, we define three types of BoW that are based on crisp, fuzzy, and possibilistic voting. The second feature representation is a generalization of the BoW and is based on Fisher Vector (FV). FV uses the Fisher Kernel principle and combines the benefits of generative and discriminative approaches. The proposed feature representations are used within two learning frameworks. The first one is supervised learning and assumes that a large collection of labeled training data is available. Within this framework, we use standard classifiers including K-nearest neighbor (K-NN), support vector machine (SVM), and Naive Bayes. The second framework is based on semi-supervised learning where only a limited amount of labeled training samples are available. We use an approach that is based on label propagation. Our proposed algorithms were evaluated using 15 medical simulation sessions. The results were analyzed and compared to those obtained using state-of-the-art algorithms. We show that our proposed speech segmentation fusion algorithms and feature mappings outperform existing methods. We also integrated all proposed algorithms and developed a GUI prototype system for subjective evaluation. This prototype processes medical simulation video and provides the user with a visual summary of the different speech segments. It also allows the user to browse videos and retrieve scenes that provide answers to semantic queries such as: who spoke and when; who interrupted who? and what was the emotion of the speaker? The GUI prototype can also provide summary statistics of each simulation video. Examples include: for how long did each person spoke? What is the longest uninterrupted speech segment? Is there an unusual large number of pauses within the speech segment of a given speaker

Robust fuzzyclustering for object recognition and classification of relational data

Prototype based fuzzy clustering algorithms have unique ability to partition the data while detecting multiple clusters simultaneously. However since real data is often contaminated with noise, the clustering methods need to be made robust to be useful in practice. This dissertation focuses on robust detection of multiple clusters from noisy range images for object recognition. Dave\u27s noise clustering (NC) method has been shown to make prototype-based fuzzy clustering techniques robust. In this work, NC is generalized and the new NC membership is shown to be a product of fuzzy c-means (FCM) membership and robust M-estimator weight (or possibilistic membership). Thus the generalized NC approach is shown to have the partitioning ability of FCM and robustness of M-estimators. Since the NC (or FCM) algorithms are based on fixed-point iteration technique, they suffer from the problem of initializations. To overcome this problem, the sampling based robust LMS algorithm is considered by extending it to fuzzy c-LMS algorithm for detecting multiple clusters. The concept of repeated evidence has been incorporated to increase the speed of the new approach. The main problem with the LMS approach is the need for ordering the distance data. To eliminate this problem, a novel sampling based robust algorithm is proposed following the NC principle, called the NLS method, that directly searches for clusters in the maximum density region of the range data without requiring the specification of number of clusters. The NC concept is also introduced to several fuzzy methods for robust classification of relational data for pattern recognition. This is also extended to non-Euclidean relational data. The resulting algorithms are used for object recognition from range images as well as for identification of bottleneck parts while creating desegregated cells of machine/ components in cellular manufacturing and group technology (GT) applications

Unsupervised tracking of time-evolving data streams and an application to short-term urban traffic flow forecasting

I am indebted to many people for their help and support I receive during my Ph.D. study and research at DIBRIS-University of Genoa. First and foremost, I would like to express my sincere thanks to my supervisors Prof.Dr. Masulli, and Prof.Dr. Rovetta for the invaluable guidance, frequent meetings, and discussions, and the encouragement and support on my way of research. I thanks all the members of the DIBRIS for their support and kindness during my 4 years Ph.D. I would like also to acknowledge the contribution of the projects Piattaforma per la mobili\ue0 Urbana con Gestione delle INformazioni da sorgenti eterogenee (PLUG-IN) and COST Action IC1406 High Performance Modelling and Simulation for Big Data Applications (cHiPSet). Last and most importantly, I wish to thanks my family: my wife Shaimaa who stays with me through the joys and pains; my daughter and son whom gives me happiness every-day; and my parents for their constant love and encouragement

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin