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  • Conference Proceeding  (42)
Type of Medium
  • Conference Proceeding  (42)
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  • 1
    Language: English
    In: 2014 22nd European Signal Processing Conference (EUSIPCO), September 2014, pp.785-789
    Description: Many modern computer vision systems combine high dimensional features and linear classifiers to achieve better classification accuracy. However, the excessively long features are often highly redundant; thus dramatically increases the system storage and computational load. This paper presents a novel feature selection algorithm, namely cardinal sparse partial least square algorithm, to address this deficiency in an effective way. The proposed algorithm is based on the sparse solution of partial least square regression. It aims to select a sufficiently large number of features, which can achieve good accuracy when used with linear classifiers. We applied the algorithm to a face recognition system and achieved the stateof- the-art results with significantly shorter feature vectors.
    Keywords: Face ; Face Recognition ; Computer Vision ; Databases ; Vectors ; Conferences ; Signal Processing Algorithms ; Feature Selection ; Sparse Partial Least Square ; Face Recognition ; Engineering
    ISSN: 2219-5491
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
    Source: IEEE eBooks
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  • 2
    Language: English
    In: 2018 41st International Conference on Telecommunications and Signal Processing (TSP), July 2018, pp.1-7
    Description: Data clustering is a fundamental machine learning problem. Community structure is common in social and biological networks. In this article we propose a novel data clustering algorithm that uses this phenomenon in mutual k - nearest neighbor (MKNN) graph constructed from the input dataset. We use the authentic scores-a metric that measures the strength of an edge in a social network graph-to rank all the edges in the MKNN graph. By removing the edges gradually in the order of their authentic scores, we collapse the MKNN graph into components to find the clusters. The proposed method has two major advantages comparing to other popular data clustering algorithms. First, it is robust to the noise in the data. Second, it finds clusters of arbitrary shape. We evaluated our algorithm on synthetic noisy datasets, synthetic 2D datasets and real-world image datasets. Results on the noisy datasets show that the proposed algorithm clearly outperforms the competing algorithms in terms of Normalized Mutual Information (NMI) scores. The proposed algorithm is the only one that does not fail on any data in the the synthetic 2D dataset, which are specifically designed to show the limitations of the clustering algorithms. When testing on the real-world image datasets, the best NMI scores achieved by the proposed algorithm is more than any other competing algorithm. The proposed algorithm has computational complexity of O(k^3n+kn\log (kn)) and space complexity of O(kn), which is better than or equivalent to the most popular clustering algorithms.
    Keywords: Clustering Algorithms ; Signal Processing Algorithms ; Machine Learning Algorithms ; Partitioning Algorithms ; Image Edge Detection ; Clustering Methods ; Social Network Services ; Data Clustering ; Authentic Score ; Graph
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 3
    Language: English
    In: 2013 IEEE International Conference on Image Processing, September 2013, pp.2489-2493
    Description: An automatic object extraction method is proposed exploiting the rich mathematical structure of quantum mechanics. First, a novel segmentation method based on the solutions of Schrödinger's equation is proposed. This powerful segmentation method allows us to model complex objects and inherent structures of edge, shape, and texture information along with the grey-level intensity uniformity, all in a single equation. Due to the large amount of segments extracted with the proposed method, the selection of the object segment is performed by maximizing a regularization energy function based on a recently proposed sub-segment analysis indicating the object boundaries. The results of the proposed automatic object extraction method exhibit such a promising accuracy that pushes the frontier in this field to the borders of the input-driven processing only - without the use of "object knowledge" aided by long-term human memory and intelligence.
    Keywords: Object Extraction ; Image Segmentation ; Schrödinger'S Equation ; Quantum Mechanics ; Applied Sciences
    ISSN: 1522-4880
    E-ISSN: 2381-8549
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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  • 4
    Language: English
    In: 21st European Signal Processing Conference (EUSIPCO 2013), September 2013, pp.1-5
    Description: The rational function systems proved to be useful in several areas including system and control theories and signal processing. In this paper, we present an extension of the well-known particle swarm optimization (PSO) method based on the hyperbolic geometry. We applied this method on digital signals to determine the optimal parameters of the rational function systems. Our goal is to minimize the error between the approximation and the original signal while the poles of the system remain stable. Namely, we show that the presented algorithm is suitable to localize the same poles by using different initial conditions.
    Keywords: Approximation Algorithms ; Vectors ; Approximation Methods ; Electrocardiography ; Particle Swarm Optimization ; Optimization ; Geometry ; Rational Functions ; Malmquist-Takenaka System ; Hyperbolic Geometry ; Particle Swarm Optimization ; Engineering
    ISSN: 2219-5491
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
    Source: IEEE eBooks
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  • 5
    Language: English
    In: 2017 Progress In Electromagnetics Research Symposium - Spring (PIERS), May 2017, pp.3258-3262
    Description: In this study, the most commonly used polarimetric SAR features including the complete coherency (or covariance) matrix information, features obtained from several coherent and incoherent target decompositions, the backscattering power and the visual texture features are compared in terms of their classification performance of different terrain classes. For pattern recognition, two powerful machine learning techniques, Collective Network of Binary Classifier (CNBC) with incremental training capability and Support Vector Machines (SVM) are employed. Each feature has its own strength and weaknesses for discriminating different SAR class types and this study aims to investigate them through incremental feature based training of both classifiers and compare the results of the experiments performed using the fully polarimetric San Francisco Bay and Flevoland datasets.
    Keywords: Scattering ; Synthetic Aperture Radar ; Matrix Decomposition ; Support Vector Machines ; Covariance Matrices ; Electromagnetics ; Springs ; Physics
    ISSN: 15599450
    E-ISSN: 19317360
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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  • 6
    Language: English
    In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 2017, pp.2587-2591
    Description: Multilabel ranking is an important machine learning task with many applications, such as content-based image retrieval (CBIR). However, when the number of labels is large, traditional algorithms are either infeasible or show poor performance. In this paper, we propose a simple yet effective multilabel ranking algorithm that is based on k-nearest neighbor paradigm. The proposed algorithm ranks labels according to the probabilities of the label association using the neighboring samples around a query sample. Different from traditional approaches, we take only positive samples into consideration and determine the model parameters by directly optimizing ranking loss measures. We evaluated the proposed algorithm using four popular multilabel datasets. The proposed algorithm achieves equivalent or better performance than other instance-based learning algorithms. When applied to a CBIR system with a dataset of 1 million samples and over 190 thousand labels, which is much larger than any other multilabel datasets used earlier, the proposed algorithm clearly outperforms the competing algorithms.
    Keywords: Signal Processing Algorithms ; Training ; Machine Learning Algorithms ; Image Retrieval ; Linear Programming ; Benchmark Testing ; Loss Measurement ; Multilabel Learning ; K-Nearest Neighbor ; Content-Based Image Retrieval ; Engineering
    ISSN: 15206149
    E-ISSN: 2379-190X
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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  • 7
    Language: English
    In: 2014 5th European Workshop on Visual Information Processing (EUVIP), December 2014, pp.1-6
    Description: Several existing content-based image retrieval and classification systems rely on low-level features which are automatically extracted from images. However, often these features lack the discrimination power needed for accurate description of the image content and hence they may lead to a poor retrieval or classification performance. This article applies an evolutionary feature synthesis method based on multi-dimensional particle swarm optimization on low-level image features to enhance their discrimination ability. The proposed method can be applied on any database and low-level features as long as some ground-truth information is available. Content-based image retrieval experiments show that a significant performance improvement can be achieved.
    Keywords: Vectors ; Feature Extraction ; Synthesizers ; Particle Swarm Optimization ; Training ; Databases ; Transforms ; Content-Based Image Retrieval ; Evolutionary Feature Synthesis ; Multi-Dimensional Particle Swarm Optimization
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 8
    Language: English
    In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015, Vol.2015, pp.2608-11
    Description: We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).
    Keywords: Algorithms ; Neural Networks (Computer)
    ISBN: 9781424492718
    ISSN: 1557-170X
    ISSN: 1094687X
    E-ISSN: 15584615
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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  • 9
    Language: English
    In: 2010 20th International Conference on Pattern Recognition, August 2010, pp.4324-4327
    Description: This paper proposes an evolutionary RBF network classifier for polar metric synthetic aperture radar ( SAR) images. The proposed feature extraction process utilizes the full covariance matrix, the gray level co-occurrence matrix (GLCM) based texture features, and the backscattering power (Span) combined with the H/α/A decomposition, which are projected onto a lower dimensional feature space using principal component analysis. An experimental study is performed using the fully polar metric San Francisco Bay data set acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared to the Wish art and a recent NN-based classifiers demonstrate the effectiveness of the proposed algorithm.
    Keywords: Classification Algorithms ; Scattering ; Artificial Neural Networks ; Covariance Matrix ; Radial Basis Function Networks ; Testing ; Accuracy ; Polarimetric Synthetic Aperture Radar ; Radial Basis Function Network ; Particle Swarm Optimization ; Dynamic Clustering ; Engineering ; Computer Science
    ISBN: 9781424475421
    ISBN: 1424475422
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
    Source: IEEE eBooks
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  • 10
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.3769-3774
    Description: In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is differentiable with respect to graph affinities, in order to optimize linear combination coefficients and parameters of several differentiable affinity functions by applying error backpropagation. We show that the learnt linear combination of affinities improves the performance over the baseline method and achieves comparable (or even better) performance when compared to the state-of-the-art salient object segmentation methods.
    Keywords: Object Detection ; Object Segmentation ; Symmetric Matrices ; Image Segmentation ; Quantum Mechanics ; Graph Theory ; Computer Vision ; Graph Affinity Learning ; Salient Object Segmentation ; Spectral Graph Theory
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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