检索结果-内蒙古大学图书馆

Multimodal Learning of Geometry-Preserving binary Codes for Semantic Image Retrieval

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2017年第4期E100D卷 600-609页

作者： Irie, Go Arai, Hiroyuki Taniguchi, Yukinobu NTT Corp Yokosuka Kanagawa 2390847 Japan Tokyo Univ Sci Tokyo 1258585 Japan

This paper presents an unsupervised approach to feature binary coding for efficient semantic image retrieval. Although the majority of the existing methods aim to preserve neighborhood structures of the feature space, semantically similar images are not always in such neighbors but are rather distributed in non-linear low-dimensional manifolds. Moreover, images are rarely alone on the Internet and are often surrounded by text data such as tags, attributes, and captions, which tend to carry rich semantic information about the images. On the basis of these observations, the approach presented in this paper aims at learning binary codes for semantic image retrieval using multimodal information sources while preserving the essential low-dimensional structures of the data distributions in the Hamming space. Specifically, after finding the low-dimensional structures of the data by using an unsupervised sparse coding technique, our approach learns a set of linear projections for binary coding by solving an optimization problem which is designed to jointly preserve the extracted data structures and multimodal data correlations between images and texts in the Hamming space as much as possible. We show that the joint optimization problem can readily be transformed into a generalized eigenproblem that can be efficiently solved. Extensive experiments demonstrate that our method yields significant performance gains over several existing methods.

关键词： image retrieval multimodal learning binary coding

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Fuzzy coding of genetic algorithms

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2003年第4期7卷 344-355页

作者： Sharma, SK Irwin, GW Queens Univ Belfast Sch Elect & Elect Engn Intelligent Syst & Control Grp Belfast Antrim North Ireland

A new chromosome encoding method, named fuzzy coding, is proposed for representing real number parameters in a genetic algorithm. Fuzzy coding provides the value of a parameter on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree of membership. Thus, it represents the knowledge associated with each parameter and is an indirect method of encoding compared with alternatives, where the parameters are directly represented in the encoding. Fuzzy coding is described and compared with conventional binary coding, gray coding, and floating-point coding. Two test examples, along with neural identification of a nonlinear pH process from experimental data, are studied. It is shown that fuzzy coding is better than the conventional methods and is effective for parameter optimization in problems where the search space is complicated.

关键词： binary coding floating-point coding fuzzy coding genetic algorithm gray coding neural networks nonlinear identification

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Enhanced embedded zerotree wavelet algorithm for lossy image coding

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IET IMAGE PROCESSING 2019年第8期13卷 1364-1374页

作者： Boujelbene, Rania Boubchir, Larbi Ben Jemaa, Yousra Univ Tunis El Manar U2S Lab Tunis Tunisia Univ Paris 08 LIASD Lab St Denis France

Embedded zerotree wavelet (EZW) algorithm is the well-known effective coding technique for low-bit-rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (NE-EZW), allowing to achieve a high compression performance in terms of peak-signal-to-noise ratio and bitrate for lossy image compression. To distribute probabilities in a more efficient way, the proposed approach is based on increasing the number of coefficients not to be encoded by the use of new symbols. Furthermore, the proposed method optimises the binary coding by the use of the compressor cell operator. Experimental results demonstrated the effectiveness of the proposed scheme over the conventional EZW and other improved EZW schemes for both natural and medical image coding applications. They have also shown that the proposed approach outperforms the most well-known algorithms, namely set partitioning in hierarchical trees (SPIHT) and JPEG2000.

关键词： wavelet transforms binary codes trees (mathematics) data compression image coding peak-signal-to-noise ratio lossy image compression binary coding conventional EZW improved EZW schemes natural image coding applications medical image coding applications lossy image coding embedded zerotree wavelet algorithm effective coding technique low-bit-rate image compression enhanced EZW high compression performance hierarchical trees JPEG2000 SPIHT

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Hybrid-coded crossover for binary-coded genetic algorithms in constrained optimization

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ENGINEERING OPTIMIZATION 2004年第1期36卷 101-122页

作者： Wu, WH Lin, CY Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei 10672 Taiwan

The success of genetic searches greatly depends on appropriate selections for many search parameters. This situation becomes more critical for optimization problems involving sharp constraint surfaces. When penalty function approaches are used to handle the constraints, the genetic algorithm conducts the search on a design space which has continually changing contours during the evolution. This often leads to incorrect gene extinctions at early stages of the search, and thus prevents the search from attaining the final global solution. This paper aims to devise a hybrid-coded crossover strategy for binary-coded genetic algorithms (BGA) which combines the major content of two-point crossover and the full domain representation characteristics embedded in many crossover schemes of real-coded genetic algorithms. It is found that the unique hybrid-coded crossover scheme can largely prevent the binary-coded genetic searches from falsely converging to sub-optimal solutions. Comparisons between BGA with the hybrid-coded crossover and the traditional two-point crossover are conducted in a number of illustrative problems with varied combinations of constraints.

关键词： genetic algorithms binary coding real coding hybrid-coded crossover constrained optimization

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An improved vector quantization method using deep neural network

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 72卷 178-183页

作者： Jiang, Wenbin Liu, Peilin Wen, Fei Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Air Force Engn Univ Air Control & Nav Inst Xian 710000 Peoples R China

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR). (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Deep neural network Vector quantization Auto-encoder binary coding

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Modulation and coding for linear Gaussian channels

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IEEE TRANSACTIONS ON INFORMATION THEORY 1998年第6期44卷 2384-2415页

作者： Forney, GD Ungerboeck, G Motorola Inc Informat Syst Grp Mansfield MA 02048 USA IBM Corp Div Res Zurich Res Lab CH-8803 Ruschlikon Switzerland

Shannon's determination of the capacity of the linear Gaussian channel has posed a magnificent challenge to succeeding generations of researchers. This paper surveys how this challenge has been met during the past half century. Orthogonal minimum-bandwidth modulation techniques and channel capacity are discussed binary coding techniques for lo low-signal-to-noise ratio (SNR) channels and nonbinary coding techniques for high-SNR channels are reviewed. Recent developments, which now allow capacity to be approached on any linear Gaussian channel, are surveyed. These new capacity-approaching techniques include turbo coding and decoding, multilevel coding, and combined coding/precoding for intersymbol-interference channels.

关键词： binary coding block codes canonical channel capacity coding gain convolutional codes lattice codes multicarrier modulation nonbinary coding orthogonal modulation precoding shaping gain single-carrier modulation trellis codes

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Evaluation of the genetic algorithm parameters on the optimization performance: a case study on pump-and-treat remediation design

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TOP 2010年第2期18卷 303-320页

作者： Gungor-Demirci, Gamze Aksoy, Aysegul Middle E Tech Univ Dept Environm Engn TR-06531 Ankara Turkey

In this study, the impacts of different crossover and encoding schemes on the performance of a genetic algorithm (GA) in finding optimal pump-and-treat (P&T) remediation designs are investigated. For this purpose, binary and Gray encodings of the decision variables are tested. Uniform and two-point crossover schemes are evaluated for two different crossover probabilities. Analysis is performed for two P&T system optimization scenarios. Results show that uniform crossover operator with Gray encoding outperforms the other alternatives for the complex problem with higher number of decision variables. On the other hand, when a simpler problem, which had a lower number of decision variables, is solved, the efficiency of GA is independent of the encoding and crossover schemes.

关键词： Optimization Genetic algorithms Uniform crossover Two-point crossover binary coding Gray coding

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GENETIC ALGORITHMS-BASED DESIGN AND OPTIMIZATION OF STATISTICAL QUALITY-CONTROL PROCEDURES

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CLINICAL CHEMISTRY 1993年第9期39卷 1972-1978页

作者： HATJIMIHAIL, AT HLTH CTR PROSOTSANE MICROBIOL LAB GR-66200 PROSOTSANE GREECE

In general, one cannot use algebraic or enumerative methods to optimize a quality-control (QC) procedure for detecting the total allowable anaLytical error with a stated probability with the minimum probability for false rejection. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and can search through large parameter spaces quickly. To explore the application of GAs in statistical QC, I developed two interactive computer programs based on the deterministic crowding genetic algorithm. Given an analytical process, the program ''Optimize'' optimizes a user-defined QC procedure, whereas the program ''Design'' designs a novel optimized QC procedure. The programs search through the parameter space and find the optimal or near-optimal solution. The possible solutions of the optimization problem are evaluated with computer simulation.

关键词： COMPUTER SIMULATION DETERMINISTIC CROWDING binary coding ERROR DETECTION STATISTICAL PROCESS MONITORING

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Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality

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NEUROINFORMATICS 2018年第3-4期16卷 339-349页

作者： Li, Zhongyu Butler, Erik Li, Kang Lu, Aidong Ji, Shuiwang Zhang, Shaoting Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA State Univ New Jersey Dept Ind & Syst Engn Piscataway NJ 08854 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

关键词： Neuron morphology Large-scale retrieval Deep learning binary coding Augmented reality

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Is the frequency noise of an oscillator of deterministic origin?

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 1996年第2期43卷 326-330页

作者： Planat, M Giordano, V Marianneau, G Vernotte, F Mourey, M Eckert, C Miehe, JA UNIV FRANCHE COMTE OBSERV BESANCONF-25044 BESANCONFRANCE ENSMM LCEPF-25030 BESANCONFRANCE CTR RECH NUCL APPL OPT GRPF-62700 STRASBOURGFRANCE

We report on a new multifractal type approach of low-frequency noise in oscillators. The time sequence is interrogated by means of a multiscale local stability exponent which acts in a sliding filtering window, Its discontinuities are reflected into a binary coding in which the correlations are analyzed. The mapping of binary data into a devil's staircase is used to emphasize the correlated and possibly deterministic origin of frequency fluctuations in quartz oscillators.

关键词： atomic clocks crystal oscillators electron device noise flicker noise fractals shot noise white noise binary coding binary data correlations deterministic origin devil's staircase discontinuities frequency fluctuations frequency noise low-frequency noise Atomic clocks Shot noise Frequency noise Low-frequency noise white noise binary code Oscillators binary data frequency fluctuations discontinuities Fractals 1/f noise Crystal oscillators

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