On the effect of calibration in classifier combination

A general approach to classifier combination considers each model as a probabilistic classifier which outputs a class membership posterior probability. In this general scenario, it is not only the quality and diversity of the models which are relevant, but the level of calibration of their estimated probabilities as well. In this paper, we study the role of calibration before and after classifier combination, focusing on evaluation measures such as MSE and AUC, which better account for good probability estimation than other evaluation measures. We present a series of findings that allow us to recommend several layouts for the use of calibration in classifier combination. We also empirically analyse a new non-monotonic calibration method that obtains better results for classifier combination than other monotonic calibration methods.     

Face detection approach from video with the aid of KPCM and improved neural network classifier

In recent years, the detection of a human face from the video has become an interesting research topic due to the video surveillance and other security issues. Efficient face detection from the video has become an immense need as it can provide various identity measures in the field of defense and other security-related areas. In our proposed method we have developed an efficient method of face detection to index a particular face from different video shots. The proposed method can be divided into Different modules. In the first module, human face from the video is extracted using segmentation technique. In our proposed method, we have used Kernel-based Possibilistic C-Means for segmentation purpose. The second module in our method is the feature extraction process where shape, LBP, and some geometrical features are extracted. The various shape features like area, circularity, and eccentricity are extracted. Once the feature values are extracted we track the particular face using forward tracking process. After the tracking process, we employ the classification technique. The classifier we utilized here is the improved neural network where the weights factors are optimized using the modified cuckoo search algorithm. The performance is compared with some existing works in order to prove the efficiency of our proposed method.     

无线Mesh网络中的P2P流媒体性能评估

通过搭建基于无线Mesh网络的P2P流媒体点播测试平台,对影响无线Mesh网络中P2P流媒体性能的流媒体编解码方式、编码速率、数据转发路径的选择和跳数四个因素进行了测试。实验结果表明,采用H.264编解码标准更适合无线Mesh网络中流媒体的传输;编码速率必须不大于网络连接速率才能获得高视频质量;P2P技术可以抵抗10%的丢包对视频质量的影响,比采用非P2P技术在视频的前1 000帧视频质量平均高出3 dB;由于P2P技术带来的流间干扰的影响使得1 000帧以后视频质量下降了6 dB,严重影响了流媒体性能;无线Mesh网络的传输能力随着跳数的增加而减弱,但是流媒体质量并未随着跳数的增加而降低。     

Correlation‐based classifier combination in the field of pattern recognition

Classifier combination methods have proved to be an effective tool to increase the performance of classification techniques that can be used in any pattern recognition applications. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still not matured enough and achieved improvements are inconsistent. In this paper, we propose a novel statistical validation technique known as correlation‐based classifier combination technique for combining classifier in any pattern recognition problem. This validation has significant influence on the performance of combinations, and their utilization is necessary for complete theoretical understanding of combination algorithms. The analysis presented is statistical in nature but promises to lead to a class of algorithms for rank‐based decision combination. The potentials of the theoretical and practical issues in implementation are illustrated by applying it on 2 standard datasets in pattern recognition domain, namely, handwritten digit recognition and letter image recognition datasets taken from UCI Machine Learning Database Repository ( http://www.ics.uci.edu/_mlearn ). 1 An empirical evaluation using 8 well‐known distinct classifiers confirms the validity of our approach compared to some other combinations of multiple classifiers algorithms. Finally, we also suggest a methodology for determining the best mix of individual classifiers.     

利用OpenGL技术提高Solaris系统下视频播放性能

通过分析Solaris系统视频播放原理,指出了图像缩放和显示速度是影响视频播放性能的重要原因,进而提出了一种利用OpenGL技术提高XWindow窗口系统下图像缩放和显示速度的方法从而提高总的视频播放性能,并给出了实现代码,最后通过实验验证了该方法的有效性.     

Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift

We consider the problem of classification in environments where training and test data may come from different probability distributions. When the fundamental stationary distribution assumption made in supervised learning (and often not satisfied in practice) does not hold, the classifier performance may significantly deteriorate. Several proposals have been made to deal with classification problems where the class priors change after training, but they may fail when the class conditional data densities also change. To cope with this problem, we propose an algorithm that uses unlabeled test data to adapt the classifier outputs to new operating conditions, without re-training it. The algorithm is based on a posterior probability model with two main assumptions: (1) the classes may be decomposed in several (unknown) subclasses, and (2) all changes in data distributions arise from changes in prior subclass probabilities. Experimental results with a neural network model on synthetic and remote sensing practical settings show that the adaptation at the subclass level can get a better adjustment to the new operational conditions than the methods based on class prior changes.     

ZFS文件系统中双容错编码性能的研究

ZFS是Sun推出的一款革新性的文件系统,它支持用户构建镜像以及单容错/双容错的软Raid。其双容错编码方案采用的是Reed-Solomon编码(RS码)。由于RS码是基于有限域运算,编码/解码时间复杂性差是其根本性的缺陷。ZFS对写操作的处理采用的是聚合后追加的方式而非传统的覆盖方式,每次写操作都会进行一次编码计算。因此,编码计算性能是影响文件系统整体性能的重要因素之一。本文的工作是将RDP这一基于奇偶校验的双容错编码与ZFS相结合,替代Reed-Solomon编码,以优化文件系统写操作的性能。我们设计了Cache优化的RDP编码算法,在ZFS中进行了实现,并通过实验验证了这一方法的有效性。     

交错BCH码在移动窄带图像信道中的性能研究

文章在详述ＢＣＨ码编译码过程的基础上，对交错ＢＣＨ码在移动瞧道压缩图像传输系统中的应用进行了计算机仿真，直观地显示出产错ＢＣＨ码在窄带图像传输系统中的优良性能，并为移动图像传输中码型的选用提供参考。     

Multi-resolution approach to human activity recognition in video sequence based on combination of complex wavelet transform,Local Binary Pattern and Zernike moment

Human activity recognition is a challenging problem of computer vision and it has different emerging applications. The task of recognizing human activities from video sequence exhibits more challenges because of its highly variable nature and requirement of real time processing of data. This paper proposes a combination of features in a multiresolution framework for human activity recognition. We exploit multiresolution analysis through Daubechies complex wavelet transform (DCxWT). We combine Local binary pattern (LBP) with Zernike moment (ZM) at multiple resolutions of Daubechies complex wavelet decomposition. First, LBP coefficients of DCxWT coefficients of image frames are computed to extract texture features of image, then ZM of these LBP coefficients are computed to extract the shape feature from texture feature for construction of final feature vector. The Multi-class support vector machine classifier is used for classifying the recognized human activities. The proposed method has been tested on various standard publicly available datasets. The experimental results demonstrate that the proposed method works well for multiview human activities as well as performs better than some of the other state-of-the-art methods in terms of different quantitative performance measures.

     

Impact of the motion and visual complexity of the background on players' performance in video game-like displays

The visual interfaces of virtual environments such as video games often show scenes where objects are superimposed on a moving background. Three experiments were designed to better understand the impact of the complexity and/or overall motion of two types of visual backgrounds often used in video games on the detection and use of superimposed, stationary items. The impact of background complexity and motion was assessed during two typical video game tasks: a relatively complex visual search task and a classic, less demanding shooting task. Background motion impaired participants' performance only when they performed the shooting game task, and only when the simplest of the two backgrounds was used. In contrast, and independently of background motion, performance on both tasks was impaired when the complexity of the background increased. Eye movement recordings demonstrated that most of the findings reflected the impact of low-level features of the two backgrounds on gaze control.     

基于多关键字匹配算法的巡检视频评价系统的研究简

针对当前巡检视频评价系统的缺陷,本文提出了基于多关键字匹配算法的巡检视频评价系统。将多关键字匹配算法应用到评论自动摘抄中,实现了对与视频相关评论的自动选择。同时结合本系统中存在多组关键字和视频标签长度无法限定等特点,对算法进行了改善与优化。     

多媒体传感器网络中的视频流传输质量分析与评估

在NS2平台上仿真实现了多媒体传感器网络的视频流传输,并在NS2-myEvalvid模型的基础上通过比较PSRN值分析了压缩量化参数、GOP类型、封包长度和封包错误率对视频流传输质量的影响。仿真实验表明,压缩量化参数、GOP长度或封包错误率取值越小,视频流的传输效果越好;而封包长度取值越小,视频流的传输效果越差。     

通过"需求配置"改进类代码的重用性能

面向对象技术并不能保证软件资产达到很好的重用性能 .通过实例分析了 Java的 Buffer库 ,发现有大量的冗余代码 .这说明了代码在类中达到的较低的重用性能 ,将会增大对类库理解、测试和维护的难度 .提出了一种基于“需求配置”来消除冗余代码的方法 ,并通过 XVCL 语言加以实现 ,结果是总的代码量减少到原有的 4 0 % .这一方法大大降低了类库的维护难度 ,同时认为通过把“需求配置”方法与面向对象技术相结合 ,可以提高软件的重用性能     

Indices for the evaluation of neural network performance as classifier: Application to structural elucidation in infrared spectroscopy

An application of Artificial Neural Networks (ANN) to the substructure detection, from infrared spectra, of organic compounds is described. Several ANNs have already been implemented for this purpose, and show promising initial results; however, many problems remain to be resolved. We wished to train ANNs to assist in a decision support system using several spectroscopic methods to elucidate the structure of unknown molecules. To optimise the ANN with respect to spectral feature extraction, network architecture, training regime and threshold determination, we have investigated several indices for use in the evaluation of network performance. Since much published work on ANN application in this field present performance indices that are poorly defined or of limited use, we recommend that the basic results be reported so that readers may calculate indices to suit their own particular needs. These basic quantities are identified and a set of derived indices recommended.     

联合站仪表控制系统安全性的模糊评价

联合站是原油集输处理的中心。仪表控制系统作为联合站的重要组成部分,对其安全评价尤其重要。文章提出采用模糊评价的方法来对联合站仪表控制系统进行安全预测和分析,以寻找其中的薄弱环节。     

Performance evaluation of the time-stamp ordering algorithm in adistributed database

Time-stamp ordering is one of the consistency preserving algorithms that is used in distributed databases. F. Baccelli (1987) has introduced a queueing model that incorporates the fork-join and resequencing synchronization constraints to analyze the algorithm's performance. The power of interpolation approximation technique is illustrated by obtaining extremely good approximations for this rather complex model. The heavy traffic approximations are obtained by showing that this model has the same diffusion limit as a system of parallel fork-join queues. The light traffic limits are obtained by applying the light traffic theory developed by M.I. Reiman and B. Simon (1989). The heavy traffic limits are computed for general arrival and service distributions, but the light traffic limits are restricted to Markovian systems     

Ergonomics in the design and evaluation of a system for "postal video letter coding"

In co-operation with the German Central Post Office for Technological Development, the authors designed and investigated the task of and workplace for "Postal video-letter-coding". Experiments including measures of performance, errors, heart rate, arrhythmia, EMG's, and EOG's were used to experiment with variations in the design of displays, codes, keyboards, shifts and rest pauses. As a result of this work the following recommendations have been made by the authors and accepted by the authorities: An optimal display configuration has been developed with two vertically arranged tv displays. Coding-rules are to be simplified by modifications of the postcode. The usual flat keyboard has been replaced by an inclined and angular keyboard. Night-shifts must be avoided because of the tedious nature of the task. A work-rest-schedule of 50 min work and 10 min rest is proposed. In 1978 the complete work-system-design will be introduced in the standard postal service.     

Performance evaluation of spatial interpolation methods in the presence of noise

This study aims to investigate the robustness of some spatial interpolation methods (SIM) in the presence of noisy data. SIM are used in different contexts such as the identification of hot-spots in brown fields, Geographical Information Systems (GIS), remote sensing and visualization/shape re-construction. In all contexts, the correct representation of a given surface is crucial. For instance, in a site characterization context identifying hot-spots, all contaminated areas should be determined in order to minimize health hazards in human use after the reclamation of the site. Here, we conduct a numerical survey on the performance of four spatial interpolation techniques using eight mathematical functions to represent domain-independent surfaces. Furthermore, we also investigate the effects of different sampling patterns on the performance of SIM.     

Robust hand detection in still video images using a combination of salient regions and color cues for interaction with an intelligent environment

The “intelligence” of an intelligent environment is not only influenced by the functionality it offers, but also largely by the naturalness and intuitiveness of its interaction modes. A very important natural interaction mode is gestures, as long as the environment’s interface poses no strict constraints on how the gestures may be performed. Since gestures are generally defined by hand/arm poses and motions, an important prerequisite to the recognition of unconstrained gestures is the robust detection of hands in video images. However, due to the strongly articulated nature of hands and the challenges given by a realistic (i.e., not strictly controlled) environment, this is a very challenging task, because it means hands need to be found in almost arbitrary configurations and under strongly varying lighting conditions. In this article, we present an approach to hand detection in the context of an intelligent house using a fusion of structural cues and color information. We first describe our detection algorithm using scale-invariant salient region features, combined with an efficient region-based filtering approach to reduce the number of false positives. The results are fused with the output of a skin color classifier. A detailed experimental evaluation on realistic data, including different cue fusing schemes, is presented. By means of an experimental evaluation on a challenging task, we demonstrate that, although each of the two different feature types (image structure and color) has drawbacks, their combination yields promising results for robust hand detection.     

Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin

For evaluation of the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product in the Zambezi Basin, daily time series (1998–2013) of 60 rain gauge stations are used. Evaluations for occurrence and rain rate are at sub-basin scale and at daily, weekly, and seasonal timescale by means of probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and frequency bias (FBS). CMORPH predicts 60% of the rainfall occurrences. Rainfall detection is better for the wet season than for the dry season. Best detection is shown for rainfall rates smaller than 2.5 mm/day. Findings on error decomposition revealed sources of Hit, Missed and False rainfall bias. CMORPH performance (detection of rainfall occurrences and estimations for rainfall depth) at sub-basin scale increases when daily estimates are accumulated to weekly estimates. Findings suggest that for the Zambezi Basin, errors in CMORPH rainfall should be corrected before the product can serve applications such as in hydrological modelling that largely rely on reliable and accurate rainfall inputs.