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  • Iosifidis, Alexandros  (5)
  • computing and processing
Type of Medium
Language
Year
  • 1
    Language: English
    In: IEEE Transactions on Neural Networks and Learning Systems, 31 May 2019, pp.1-15
    Description: The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational perceptron (GOP) was proposed to extend the conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons. Together with GOP, a progressive operational perceptron (POP) algorithm was proposed to optimize a predefined template of multiple homogeneous layers in a layerwise manner. In this paper, we propose an efficient algorithm to learn a compact, fully heterogeneous multilayer network that allows each individual neuron, regardless of the layer, to have distinct characteristics. Based on the complexity of the problem, the proposed algorithm operates in a progressive manner on a neuronal level, searching for a compact topology, not only in terms of depth but also width, i.e., the number of neurons in each layer. The proposed algorithm is shown to outperform other related learning methods in extensive experiments on several classification problems.
    Keywords: Neurons ; Biological Neural Networks ; Network Topology ; Learning Systems ; Topology ; Computational Modeling ; Nonhomogeneous Media ; Architecture Learning ; Feedforward Network ; Generalized Operational Perceptron (GOP) ; Progressive Learning ; Computer Science
    ISSN: 2162-237X
    E-ISSN: 2162-2388
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  • 2
    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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  • 3
    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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  • 4
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.2276-2281
    Description: Aquatic macroinvertebrate biomonitoring is an efficient way of assessment of slow and subtle anthropogenic changes and their effect on water quality. It is imperative to have reliable identification and counts of the various taxa occurring in samples as these form the basis for the quality indices used to infer the ecological status of the aquatic ecosystem. In this paper, we try to close the gap between human taxa identification accuracy (typically 90-95% on 30-40 classes of macroinvertebrates) and results of automatic fine-grained classification by introducing a novel technique based on Convolutional Neural Networks (CNN). CNN learns optimal features for macroinvertebrate classification and achieves near human accuracy when tested on 29 macroinvertebrate classes. Moreover, we perform comparative evaluation of the learned features against the hand-crafted features, which have been commonly used in classical approaches, and confirm superiority of the learned deep features over the engineered ones.
    Keywords: Feature Extraction ; Ecosystems ; Water Resources ; Machine Vision ; Microscopy ; Databases
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 5
    Language: English
    In: 2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI), December 2016, pp.43-48
    Description: The types and numbers of benthic macroinvertebrates found in a water body reflect water quality. Therefore, macroinvertebrates are routinely monitored as a part of freshwater ecological quality assessment. The collected macroinvertebrate samples are identified by human experts, which is costly and time-consuming. Thus, developing automated identification methods that could partially replace the human effort is important. In our group, we have been working toward this goal and, in this paper, we improve our earlier results on automated macroinvertebrate classification obtained using deep Convolutional Neural Networks (CNNs). We apply simple data enrichment prior to CNN training. By rotations and mirroring, we create new images so as to increase the total size of the image database sixfold. We evaluate the effect of data enrichment on Caffe and MatConvNet CNN implementations. The networks are trained either fully on the macroinvertebrate data or first pretrained using ImageNet pictures and then fine-tuned using the macroinvertebrate data. The results show 3-6% improvement, when the enriched data are used. This is an encouraging result, because it significantly narrows the gap between automated techniques and human experts, while it leaves room for future improvements as even the size of the enriched data, about 60000 images, is small compared to data sizes typically required for efficient training of deep CNNs.
    Keywords: Training ; Monitoring ; Feature Extraction ; Databases ; Testing ; Quality Assessment ; Convolution
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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