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  • signal processing and analysis  (14)
  • computing and processing
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  • 1
    Language: English
    In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), August 2012, Vol.42(4), pp.1169-1186
    Description: Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a "Divide and Conquer" type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.
    Keywords: Neurons ; Scattering ; Covariance Matrix ; Training ; Matrix Decomposition ; Indexes ; Measurement ; Evolutionary Classifiers ; Multidimensional Particle Swarm Optimization (MD-Pso) ; Polarimetric Synthetic Aperture Radar (SAR) ; Sciences (General) ; Engineering
    ISSN: 1083-4419
    E-ISSN: 1941-0492
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  • 2
    Language: English
    In: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, April 2010, Vol.40(2), pp.298-319
    Description: In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex multimodal optimization problems at high dimensions. The first one, which is the so-called multidimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in an MD search space, where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. Among many PSO variants in the literature, none yields a robust solution, particularly over multimodal complex problems at high dimensions. To address this problem, we propose the fractional global best formation (FGBF) technique, which basically collects all the best dimensional components and fractionally creates an artificial global best (aGB) particle that has the potential to be a better "guide" than the PSO's native gbest particle. This way, the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. We investigated both individual and mutual applications of the proposed techniques over the following two well-known domains: 1) nonlinear function minimization and 2) data clustering. An extensive set of experiments shows that in both application domains, MD PSO with FGBF exhibits an impressive speed gain and converges to the global optima at the true dimension regardless of the search space dimension, swarm size, and the complexity of the problem.
    Keywords: Particle Swarm Optimization ; Multidimensional Systems ; Organisms ; Genetic Programming ; Particle Tracking ; Robustness ; Stochastic Processes ; Multidimensional Signal Processing ; Computer Simulation ; Fractional Global Best Formation (Fgbf) ; Multidimensional (MD) Search ; Particle Swarm Optimization (Pso) ; Sciences (General) ; Engineering;
    ISSN: 10834419
    E-ISSN: 1941-0492
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  • 3
    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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  • 4
    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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  • 5
    Language: English
    In: 2015 IEEE International Conference on Image Processing (ICIP), September 2015, pp.1692-1696
    Description: In this study, we propose an unsupervised, state-of-the-art saliency map generation algorithm which is based on a recently proposed link between quantum mechanics and spectral graph clustering, Quantum Cuts. The proposed algorithm forms a graph among superpixels extracted from an image and optimizes a criterion related to the image boundary, local contrast and area information. Furthermore, the effects of the graph connectivity, superpixel shape irregularity, superpixel size and how to determine the affinity between superpixels are analyzed in detail. Furthermore, we introduce a novel approach to propose several saliency maps. Resulting saliency maps consistently achieves a state-of-the-art performance in a large number of publicly available benchmark datasets in this domain, containing around 18k images in total.
    Keywords: Image Edge Detection ; Quantum Mechanics ; Optimization ; Image Color Analysis ; Eigenvalues and Eigenfunctions ; Shape ; Approximation Methods
    ISSN: 15224880
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 6
    Language: English
    In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), December 2016, pp.1-6
    Description: The k-nearest-neighbour classifiers (k-NN) have been one of the simplest yet most effective approaches to instance based learning problem for image classification. However, with the growth of the size of image datasets and the number of dimensions of image descriptors, popularity of k-NNs has decreased due to their significant storage requirements and computational costs. In this paper we propose a vector quantization (VQ) based k-NN classifier, which has improved efficiency for both storage requirements and computational costs. We test the proposed method on publicly available large scale image datasets and show that the proposed method performs comparable to traditional k-NN with significantly better complexity and storage requirements.
    Keywords: Vector Quantization ; Training ; Image Classification ; Computational Efficiency ; Classification Algorithms ; Feature Extraction ; K-NN Classifier ; Vector Quantization ; Large-Scale Image Classification ; Applied Sciences
    E-ISSN: 2154-512X
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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  • 7
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.3645-3649
    Description: Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces a joint multi-layer K-Means clustering solution for determination of the codebooks. The performance of the proposed method is improved further by a joint encoding scheme. Experimental results verify the success of the proposed algorithm as it outperforms the state-of-the-art methods.
    Keywords: Encoding ; Training ; Hamming Distance ; Optimization ; Vector Quantization ; Search Problems
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 8
    Language: English
    In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, October 2015, pp.2130-2133
    Description: Devices equipped with accelerometer sensors such as today's mobile devices can make use of motion to exchange information. A typical example for shared motion is shaking of two devices which are held together in one hand. Deriving a shared secret (key) from shared motion, e.g. for device pairing, is an obvious application for this. Only the keys need to be exchanged between the peers and neither the motion data nor the features extracted from it. This makes the pairing fast and easy. For this, each device generates an information signal (key) independently of each other and, in order to pair, they should be identical. The key is essentially derived by quantizing certain well discriminative features extracted from the accelerometer data after an implicit synchronization. In this paper, we aim at finding a small set of effective features which enable a significantly simpler quantization procedure than the prior art. Our tentative results with authentic accelerometer data show that this is possible with a competent accuracy (76%) and key strength (entropy approximately 15 bits).
    Keywords: Acceleration ; Feature Extraction ; Accelerometers ; Sensors ; Quantization (Signal) ; Protocols ; Kernel ; Accelerometer ; Feature Extraction ; Quantization ; Information Signal
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  • 9
    Language: English
    In: 2015 International Conference on Information and Communication Technology Research (ICTRC), May 2015, pp.116-119
    Description: In this paper, we propose an improved spectrum access algorithm for cognitive radio applications using a Hidden Markov Model (HMM) for learning the primary user channel usage pattern. The proposed scheme maximizes the channel utilization without causing significant interference to the primary user. Simulation results show that the proposed algorithm provides about 3 times improvement in channel utilization compared to the system proposed in [1], with a slight degradation in collision probability. It is also observed that the proposed scheme performance is robust to variations in the primary user behavior.
    Keywords: Hidden Markov Models ; Cognitive Radio ; Prediction Algorithms ; Sensors ; Interference ; Signal to Noise Ratio ; Cognitive Radio ; Hidden Markov Models ; Spectrum Access
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 10
    Language: English
    In: 2018 25th IEEE International Conference on Image Processing (ICIP), October 2018, pp.311-315
    Description: The massive size of data that needs to be processed by Machine Learning models nowadays sets new challenges related to their computational complexity and memory footprint. These challenges span all processing steps involved in the application of the related models, i.e., from the fundamental processing steps needed to evaluate distances of vectors, to the optimization of large-scale systems, e.g. for non-linear regression using kernels, or the speed up of deep learning models formed by billions of parameters. In order to address these challenges, new approximate solutions have been recently proposed based on matrix/tensor decompositions, randomization and quantization strategies. This paper provides a comprehensive review of the related methodologies and discusses their connections.
    Keywords: Kernel ; Matrix Decomposition ; Training ; Computational Modeling ; Quantization (Signal) ; Eigenvalues and Eigenfunctions ; Artificial Neural Networks ; Approximate Nearest Neighbor Search ; Vector Quantization ; Hashing ; Approximate Kernel-Based Learning ; Low-Rank Approximation ; Neural Network Acceleration ; Applied Sciences
    ISSN: 15224880
    E-ISSN: 2381-8549
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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