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arXiv 2018年

作者： Deǧerli, Ayşen Aslan, Sinem Yamaç, Mehmet Sankur, Bülent Gabbouj, Moncef Tampere University of Technology Laboratory of Signal Processing Tampere Finland Ca' Foscari University of Venice European Centre for Living Technology Venice Italy Ege University International Computer Institute Izmir Turkey Boǧaziçi University Electrical and Electronics Engineering Department Istanbul Turkey

—Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing

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Combing context and commonsense knowledge through neural networks for solving winograd schema problems

Combing context and commonsense knowledge through neural net...

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2017 AAAI Spring Symposium

作者： Liu, Quan Jiang, Hui Ling, Zhen-Hua Zhu, Xiaodan Wei, Si Hu, Yu National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui China Department of Electrical Engineering and Computer Science York University Canada National Research Council Canada Ottawa Canada IFLYTEK Research Hefei China

ISBN: (纸本)9781577357797

This paper proposes a general framework to combine context and commonsense knowledge for solving the Winograd Schema (WS) and Pronoun Disambiguation Problems (PDP). In the proposed framework, commonsense knowledge bases (e.g. cause-effect word pairs) are quantized as knowledge constraints. The constraints guide us to learn knowledge enhanced embeddings (KEE) from large text corpus. Based on the pre-trained KEE models, this paper proposes two methods to solve the WS and PDP problems. The first method is an unsupervised method, which represents all the pronouns and candidate mentions in continuous vector spaces based on their contexts and calculates the semantic similarities between all the possible word pairs. The pronoun disambiguation procedure could then be implemented by comparing the semantic similarities between the pronoun (to be resolved) and all the candidate mentions. The second method is a supervised method, which extracts features for all the pronouns and candidate mentions and solves the WS problems by training a typical mention pair classification model. Similar to the first method, the features used in the second method are also extracted based on the KEE models. Experiments conducted on the available PDP and WS test sets show that, these two methods both achieve consistent improvements over the baseline systems. The best performance reaches 62% in accuracy on the PDP test set of the first Winograd Schema Challenge. © Copyright 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Vector spaces

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A computationally efficient detector: Using both the test and training data for disturbance correlation estimation

A computationally efficient detector: Using both the test an...

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2017 International Conference on Radar Systems, Radar 2017

作者： Liu, Jun Zhao, Hong-Yan Liu, Weijian Zhu, Shengqi Liu, Hongwei Li, Hongbin National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China Wuhan Electronic Information Institute Wuhan430019 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9781785615153

This paper examines a target detection problem in colored Gaussian disturbance with an unknown covariance matrix. In many classic adaptive detectors, the covariance estimator is formed by using only the training data. This necessitates calculating a new covariance estimator for each cell under test (CUT) during the cell-by-cell target search process. We consider herein an alternative approach that forms the covariance matrix estimate by using both test and training data for detection in homogeneous environments. This approach is computationally much more efficient since the covariance matrix estimator is computed only once and can be applied for target detection at each CUT. Using this estimator, we propose a new detector with two tunable parameters, which includes several existing detectors as special cases. Closed-form expressions for the probabilities of false alarm and detection are derived in the matched and mismatched cases. Simulation results reveal that the rejection capability of mismatched signals of the proposed detector can be flexibly controlled by adjusting its tunable parameters. © 2017 Institution of engineering and Technology. All rights reserved.

关键词： Covariance matrix

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Multilinear class-specific discriminant analysis

arXiv

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arXiv 2017年

作者： Tran, Dat Thanh Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University

There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations. However, there has not been any attempt to employ class-specific discrimination criteria for the tensor data. In this paper, we propose a multilinear subspace learning technique suitable for applications requiring class-specific tensor models. The method maximizes the discrimination of each individual class in the feature space while retains the spatial structure of the input. We evaluate the efficiency of the proposed method on two problems, i.e. facial image analysis and stock price prediction based on limit order book data. Copyright © 2017, The Authors. All rights reserved.

关键词： Tensors

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Improving efficiency in convolutional neural network with multilinear filters

arXiv

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arXiv 2017年

作者： Tran, Dat Thanh Iosifidis, Alexandros Gabbouj, Moncef Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Aarhus Denmark

The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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A study of speaker verification performance with expressive speech

A study of speaker verification performance with expressive ...

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IEEE International Conference on Acoustics, speech and signal processing

作者： Srinivas Parthasarathy Chunlei Zhang John H.L. Hansen Carlos Busso Multimodal Signal Processing (MSP) Laboratory - Center for Robust Speech Systems (CRSS) Department of Electrical Engineering The University of Texas at Dallas Richardson 75080 USA

ISBN: (纸本)9781509041183

Expressive speech introduces variations in the acoustic features affecting the performance of speech technology such as speaker verification systems. It is important to identify the range of emotions for which we can reliably estimate speaker verification tasks. This paper studies the performance of a speaker verification system as a function of emotions. Instead of categorical classes such as happiness or anger, which have important intra-class variability, we use the continuous attributes arousal, valence, and dominance which facilitate the analysis. We evaluate an speaker verification system trained with the i-vector framework with a probabilistic linear discriminant analysis (PLDA) back-end. The study relies on a subset of the MSP-PODCAST corpus, which has naturalistic recordings from 40 speakers. We train the system with neutral speech, creating mismatches on the testing set. The results show that speaker verification errors increase when the values of the emotional attributes increase. For neutral/moderate values of arousal, valence and dominance, the speaker verification performance are reliable. These results are also observed when we artificially force the sentences to have the same duration.

关键词： Speaker verification emotion recognition speaker recognition verification system emotion recognition Emotions speech

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A Subpixel Target Detection Approach to Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2017年第9期55卷 5093-5114页

作者： Xue, Bai Yu, Chunyan Wang, Yulei Song, Meiping Li, Sen Wang, Lin Chen, Hsian-Min Chang, Chein-I Center for Hyperspectral Imaging in Remote Sensing Information and Technology College Dalian Maritime University Dalian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Key Laboratory of Spectral Imaging Technology Chinese Academy of Sciences Xi'an China State Key Laboratory of Integrated Services Networks Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science and Information Management Providence University Taichung02912 Taiwan

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral-spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu's method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. © 1980-2012 IEEE.

关键词： Support vector machines

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Comments on "Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization"

arXiv

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arXiv 2018年

作者： Khan, Shujaat Wahab, Abdul Naseem, Imran Moinuddin, Muhammad Bio-imaging and Signal Processing Laboratory Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Daejeon Yuseong-gu34141 Korea Republic of Department of Mathematics University of Education Attock Campus43600 Pakistan School of Electrical Electronic and Computer Engineering University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia College of Engineering Karachi Institute of Economics and Technology Korangi Creek Karachi75190 Pakistan King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering Department King Abdulaziz University Jeddah Saudi Arabia

The purpose of this note is to highlight some critical flaws in recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in "Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization" [Non-linear Dyn. 88(2), 839-858 (2017)]. It is substantiated that these algorithms do not always converge whereas they have no advantage over the CLMS and NLMS algorithms whenever they converge. Our claims are based on analytical reasoning supported by simulations. Copyright © 2018, The Authors. All rights reserved.

关键词： Equalizers

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Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy

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Procedia computer Science 2017年 115卷 440-447页

作者： Gajanan M. Galshetwar Laxman M. Waghmare Anil B. Gonde Subrahmanyam Murala Center of Excellence in Signal and Image Processing (COESIP) Department of ECE SGGSIET Nanded Maharashtra 431606 India Computer Vision and Pattern Recognition Laboratory Department of Electrical Engineering IIT Ropar Rupnagar 140001 India

In this paper, a novel approach for content based image retrieval (CBIR) in diabetic retinopathy (DR) is proposed. The concept of salient point selection and inter-plane relationship technique is used. Salient points are selected from edgy image and later using inter-planer relationship, Local Binary Patterns (LBPs) are calculated using the salient point as a center pixel. Our approach enhanced the results as we used color features in combination with LBP features. Experimentation is carried out on MESSIDOR database of 1200 retinal images, proposed approach has average precision of 57.82% as compared to the earlier approach whose average precision is 53.70%.

关键词： Content-Based image retrieval (CBIR) Diabetic Retinopathy (DR) Edgy Salient points Local Binary Patterns (LBPs)

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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