C. Barbu

Tulane University, New Orleans, Louisiana, United States

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Publications (18)0 Total impact

  • Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part II; 01/2011
  • Costin Barbu, Jing Peng, Guna Seetharaman
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    ABSTRACT: Ensemble methods provide a principled framework for building high performance classifiers and representing many types of data. As a result, these methods can be useful for making inferences in many domains such as classification and multi-modal biometrics. We introduce a novel ensemble method for combining multiple representations (or views). The method is a multiple view generalization of AdaBoost. Similar to AdaBoost, base classifiers are independently built from each representation. Unlike AdaBoost, however, all data types share the same sampling distribution as the view whose weighted training error is the smallest among all the views. As a result, the most consistent data type dominates over time, thereby significantly reducing sensitivity to noise. In addition, our proposal is provably better than AdaBoost trained on any single type of data. The proposed method is applied to the problems of facial and gender prediction based on biometric traits as well as of protein classification. Experimental results show that our method outperforms several competing techniques including kernel-based data fusion.
    01/2010;
  • Costin Barbu, Jing Peng, Richard Sims
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    ABSTRACT: Ensemble methods provide a principled framework in which to build high performance classifiers and represent many types of data. As a result, these methods can be useful for making inferences about biometric and biological events. We introduce a novel ensemble method for combining multiple representations (or views). The method is a multiple view generalization of AdaBoost. Similar to AdaBoost, base classifiers are independently built from each represetation. Unlike AdaBoost, however, all data types share the same sampling distribution computed from the base classifier having the smallest error rate among input sources. As a result, the most consistent data type dominates over time, thereby significantly reducing sensitivity to noise. The method is applied to the problem of facial and gender prediction based on biometric traits. The new method outperforms several competing techniques including kernel-based data fusion, and is provably better than AdaBoost trained on any single type of data.
    Proc SPIE 04/2007;
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    ABSTRACT: Component based object detection approaches have been shown to significantly improve object detection performance in adversities such as occlusion, variations in pose, in and out of plane rotation and poor illumination. Even the best object detectors are prone to errors when used in a global object detection scheme (one that uses the whole object as a single entity for detection purpose), due to these problems. We propose a fuzzy approach to object detection that treats an object as a set of constituent components rather than a single entity. The object detection task is completed in two steps. In the first step, candidates for respective components are selected based on their appearance match and handed over to the geometrical configuration classifier. The geometrical configuration classifier is a fuzzy inference engine that selects one candidate for each component such that each candidate is a reasonable match to the corresponding component in terms of appearance and also a good fit for the overall geometrical model. The detected object consists of candidates that are not necessarily the best in terms of appearance match or the closest to the geometrical model in terms of placement. The output is a set of candidates that is an optimal combination satisfying both criteria. We evaluate the technique on a well known face dataset and show that the technique results in detection of most faces in a scale-invariant manner. The technique has been shown to be robust to in-plane rotations and occlusion.
    Int. J. Approx. Reasoning. 01/2007; 45:546-563.
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    C. Barbu, M. Lohrenz, G. Layne
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    ABSTRACT: The smart management of clutter is a key component in designing intelligent, next-generation user interfaces and electronic displays. Intelligent devices can enhance a user's situational awareness under adverse conditions. In this paper we present two approaches to assist a user with target detection and clutter analysis, and we suggest how these tools could be integrated with an electronic chart system. The first tool, an information fusion technique, is a multiple-view generalization of AdaBoost, which can assist a user in finding a target partially obscured by display clutter. The second technique clusters geospatial features on an electronic display and determines a meaningful measure of display clutter. The clutter metric correlates with preliminary, subjective, clutter rankings. The metric can be used to warn a user if display clutter is a potential hazard for his performance. We compare the performance of the proposed techniques with recent classifier fusion strategies on a set of synthetic data.
    Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on; 11/2006
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    C. Barbu, R. Iqbal, Jing Peng
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    ABSTRACT: A clever information fusion algorithm is a key component in designing a robust multimodal biometrics algorithm. We present a novel information fusion approach that can be a very useful tool for multimodal biometrics learning. The proposed technique is a multiple view generalization of AdaBoost in the sense that weak learners from various information sources are selected in each iteration based on lowest weighted error rate. Weak learners trained on individual views in each iteration rectify the bias introduced by learners in preceding iterations resulting in a self regularizing behavior. We compare the classification performance of proposed technique with recent classifier fusion strategies in various domains such as face detection, gender classification and texture classification.
    Computer Vision and Pattern Recognition Workshop, 2006. CVPRW '06. Conference on; 07/2006
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    Costin Barbu, Maura Lohrenz, Geary Layne
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    ABSTRACT: Intelligent devices, with smart clutter management capabilities, can enhance a user's situational awareness under adverse conditions. Two approaches to assist a user with target detection and clutter analysis are presented, and suggestions on how these tools could be integrated with an electronic chart system are further detailed. The first tool, which can assist a user in finding a target partially obscured by display clutter, is a multiple-view generalization of AdaBoost. The second technique determines a meaningful measure of clutter in electronic displays by clustering features in both geospatial and color space. The clutter metric correlates with preliminary, subjective, clutter ratings. The user can be warned if display clutter is a potential hazard to performance. Synthetic and real data sets are used for performance evaluation of the proposed technique compared with recent classifier fusion strategies
    The Fifth International Conference on Machine Learning and Applications, ICMLA 2006, Orlando, Florida, USA, 14-16 December 2006; 01/2006
  • C. Barbu, R. Iqbal, Jing Peng
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    ABSTRACT: We present a new framework for classifier fusion that uses a shared sampling distribution for obtaining a weighted classifier ensemble. The weight update process is self regularizing as subsequent classifiers trained on the disjoint views rectify the bias introduced by any classifier in preceding iterations. We provide theoretical guarantees that our approach indeed provides results which are better than the case when boosting is performed separately on different views. The results are shown to outperform other classifier fusion strategies on a well known texture image database.
    Data Mining, Fifth IEEE International Conference on; 12/2005
  • C. Barbu, R.E. Trahan
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    ABSTRACT: The purpose of this paper is to address the full estimation issue, where both the order and the parameters are identified for single-input/single-output linear time-invariant systems. The technique introduced here investigates the system identification problem when the phase information is lacking and only the magnitude of the input/output and the frequency data are available. The system identification approach described in this work has been shown to function with good results on both simulated and measured data
    Control Applications, 2005. CCA 2005. Proceedings of 2005 IEEE Conference on; 10/2005
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    ABSTRACT: In this paper we investigate the performance of boosting used for fusing various classifiers. We propose a new boosting - based algorithm for fusion and we show through empirical studies on texture image data sets that it outperforms existing SVM-based classifier fusion technique in terms of accuracy, computational efficiency and robustness.
    Information Reuse and Integration, Conf, 2005. IRI -2005 IEEE International Conference on.; 09/2005
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    ABSTRACT: Bathymetry is used to determine optimal tactics during Mine Warfare operations. Previous work demonstrated that bathymetric data could be acquired from the Volume Search Sonar (VSS) mounted on the AQS-20 system. The VSS transmitter produces a pulse at approximately one-second intervals along the track. The returning pulse from the sea-bottom is received by a group of sensors and beamformed in hardware into two fans (one pitched slightly forward and a second pitched slightly aft). A possible way to increase the accuracy of the bathymetry data is to improve the angle of arrival estimates by processing the adjacent across-track and/or along-track beam pairs. This paper employs narrow-beams monopulse techniques in order to investigate improvements to the bathymetric data over conventional processing. A comparative analysis of the experimental results for both the new and the classical technique is presented.
    OCEANS, 2005. Proceedings of MTS/IEEE; 02/2005
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    Conference Proceeding: Meta latent semantic analysis
    M. Simina, C. Barbu
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    ABSTRACT: Meta latent semantic analysis (MLSA) is a novel approach to automated document analysis and indexing which relies on symbolic ontologies to further enhance the traditional probabilistic latent semantic analysis (LSA) of documents. While LSA is able to discover clusters of related terms and documents in a given collection of documents, the proposed MLSA is able to meta-cluster such clusters by taking into account existing symbolic ontologies relevant for the analyzed collections of documents. Such an approach can be successfully used to improve the performance of fast LSA by random projection.
    Systems, Man and Cybernetics, 2004 IEEE International Conference on; 11/2004
  • M. Simina, A. Edelman, C. Barbu
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    ABSTRACT: This paper presents a specialized search engine for image retrieval called ImageScour. Compared with a traditional search engine, ImageScour takes advantage of contextual and topological information about the requested image to improve its retrieval performance. In particular, ImageScour is relevant for automatically repairing "broken" links pointing to other Websites, when the content of these Websites is updated or reorganized. Empirical results show that ImageScour outperforms general search engines (e.g., the advanced Google image search) by a significant margin.
    Systems, Man and Cybernetics, 2004 IEEE International Conference on; 11/2004
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    ABSTRACT: An adaptive algorithm is proposed in this work for teaming the user profile based on his initial profile and on queries' interpretation using fuzzy concept hierarchies. The dynamics of the user profile is modeled by employing a new concept, time-words vector hyperspace. The preliminary results from applying this new approach are promising. Future plans and recommendations for further expanding are provided.
    Systems, Man and Cybernetics, 2004 IEEE International Conference on; 11/2004
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    ABSTRACT: Predicting the degree of burial of mines in soft sediments is one of the main concerns of Naval Mine CounterMeasures (MCM) operations. This is a difficult problem to solve due to uncertainties and variability of the sediment parameters (i.e., density and shear strength) and of the mine state at contact with the seafloor (i.e., vertical and horizontal velocity, angular rotation rate, and pitch angle at the mudline). A stochastic approach is proposed in this paper to better incorporate the dynamic nature of free-falling cylindrical mines in the modeling of impact burial. The orientation, trajectory and velocity of cylindrical mines, after about 4 meters free-fall in the water column, are very strongly influenced by boundary layer effects causing quite chaotic behavior. The model's convolution of the uncertainty through its nonlinearity is addressed by employing Monte Carlo simulations. Finally a risk analysis based on the probability of encountering an undetectable mine is performed.
    Proc SPIE 09/2004;
  • Rafal A. Angryk, Costin Barbu
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    ABSTRACT: In this paper we investigate the role of the user profile in information filtering and we introduce a novel algorithm for learning the user profile based on user's initial profile and on a queries' interpretation using fuzzy generalization (Angryk and Petry 2003). Thousands of documents are usually retrieved by search engines for a given query during an information search on WWW. One way to prune irrelevant documents is to take advantage of the user's implicit interests to filter the documents returned by the search engine, or to reformulate the query based on these interests. One of the common representations of the documents (and queries) in information retrieval is based on the vector hyperspace model (Salton and McGill 1993). We are using the expanded version of Salton's vector space model introduced by Barbu and Simina (2003) for its effectiveness in computing the dynamics of the user profile. In contrast with the classical vector space model, this recent model, time-words vector hyperspace, has an additional temporal dimension. The coordinates of the documents and queries vectors are calculated using the traditional TF-IDF technique. Only the queries have a temporal dimension (current interest weight) which is set to a preset positive initial value that decays in time, suggesting that some specific user interests could decrease as time goes on. The user's categories of interest are computed based on a novel approach using Fuzzy Concept Hierarchies (FCH) extracted from the WordNet1 ontology. An FCH is built for each of the query's keywords, using their hypernym chains provided by WordNet. A sub- unitary membership degree (weight) is assigned for each of the edges of the hierarchy via a bottom-up approach, from the lowest level concept (initial keyword) to the more abstract one. The assignment of the weights is performed according to the following two conditions: 1) The sum of weights assigned to the links outgoing from the original keyword has to be equal to unity. 1
    Proceedings of the Nineteenth National Conference on Artificial Intelligence, Sixteenth Conference on Innovative Applications of Artificial Intelligence, July 25-29, 2004, San Jose, California, USA; 01/2004
  • C. Barbu, M. Simina
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    ABSTRACT: This paper presents an algorithm for learning incrementally the profile of a user, based on an (optional) initial user profile and on user's queries using probabilistic latent semantic analysis. A new model, a temporal latent semantic space, is introduced in order to keep track of the user's interests' changes. The results of the retrieval experiments using this new algorithm are compared with other traditional information retrieval techniques.
    Systems, Man and Cybernetics, 2003. IEEE International Conference on; 11/2003
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    Costin Barbu, Marin Simina
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    ABSTRACT: This paper presents an adaptive algorithm for learning the user profile. The user profile is learned incrementally and continuously based on user's initial profile, his actions and on semantic interpretation of queries using hypernyms extracted by WordNet. A novel model, time - words vector hyperspace, is introduced in order to keep track of the user's interests changes. This new model is achieved by adding a temporal dimension to the classical vector hyperspace model. The results of the retrieval experiments using this new algorithm show an improved effectiveness over the current information retrieval techniques.
    Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, May 12-14, 2003, St. Augustine, Florida, USA; 01/2003