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1.
Detection of anomalies is a broad field of study, which is applied in different areas such as data monitoring, navigation, and pattern recognition. In this paper we propose two measures to detect anomalous behaviors in an ensemble of classifiers by monitoring their decisions; one based on Mahalanobis distance and another based on information theory. These approaches are useful when an ensemble of classifiers is used and a decision is made by ordinary classifier fusion methods, while each classifier is devoted to monitor part of the environment. Upon detection of anomalous classifiers we propose a strategy that attempts to minimize adverse effects of faulty classifiers by excluding them from the ensemble. We applied this method to an artificial dataset and sensor-based human activity datasets, with different sensor configurations and two types of noise (additive and rotational on inertial sensors). We compared our method with two other well-known approaches, generalized likelihood ratio (GLR) and One-Class Support Vector Machine (OCSVM), which detect anomalies at data/feature level.  相似文献   

2.
A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single classifier. In the latter, two classifiers are employed, one per sensor‐feature space, which were offline selected based on information theory and fused by a trainable fusion method applied over the likelihoods provided by the component classifiers. The proposed schemes for sensor combination, and more specifically the trainable fusion method, lead to enhanced detection performance and, in addition, maintenance of false‐alarms under tolerable values in comparison with single‐based classifiers. Experimental results highlight the performance and effectiveness of the proposed pedestrian detection system and the related sensor data combination strategies. © 2009 Wiley Periodicals, Inc.  相似文献   

3.
Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.  相似文献   

4.
针对度量层输出的多分类器融合,该文提出一种基于Multi-agent思想的融合算法。该算法给出样本集在多分类器下的偏好判断矩阵概念,可以根据各个样本的具体情况自适应地为各分类器赋予权值。实验证明,该算法可得到比其他方法更低的分类错误率。  相似文献   

5.
ABSTRACT

Nowadays, accurate spectral reflectance information is provided by hyperspectral (HS) data while light detection and ranging (lidar) data provides precise information about the height and geometrical properties of the surfaces. In the most research papers, data fusion of disparate sensors significantly improves object classification performance compared to that of just an individual sensor. Previous researches on fusion of these two sensors had problems such as crisp classifiers or simple fuzzy decision-making systems. This article tries to overcome these weaknesses by accurate support vector machine (SVM) and Fuzzy SVM as classifiers in crisp and fuzzy decision fusion system and fusion of two sensors by two different methods based on precise theories of Bayesian and Shafer. Also, the proposed method tries to compare the results of fusion of both data using decision fusion system with stacked features strategy. This study focuses on HS and lidar fusion through three main phases. The first phase is based on the using of Noise Weighted Harsanyi-Farrand-Chang method and principal component analysis to overcome the high dimensionality problem of HS data. The second phase is based on the feature extraction and selection strategy on lidar data. Finally, fuzzy SVM and Dempster Shafer methods are applied as fuzzy classification and fuzzy decision fusion strategies on the feature spaces. A co-registered HS and lidar data set from Houston of U.S.A. by 15 classes was available to examine the effectiveness of the proposed method. The results of this study highlight that the combination of HS and lidar data enable reliable mapping of land cover.  相似文献   

6.
Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported.  相似文献   

7.
Genetic algorithm (GA) has been used as a conventional method for classifiers to evolve solutions adaptively for classification problems. In this paper, a new approach using class decomposition is proposed to improve the performance of GA-based classifiers. A classification problem is fully partitioned into several class modules in the output domain and each module is responsible for solving a fraction of the original problem. These modules are trained in parallel and independently and the results obtained are integrated and evolved further for a final solution. A scheme based on Fisher's linear discriminant (FLD) computation is used to estimate the difficulty of separating two classes. Based on the FLD information derived, different integration approaches are proposed and their performance is compared. The experiment results on a benchmark data set show that class decomposition can achieve higher classification rate than the normal GA and FLD-based integration improves the classification accuracy further.  相似文献   

8.

Detection of bare-hand under non-ideal conditions is a challenging task. Most of the existing hand detection systems are developed under limited environmental constraints. In this study, a robust two-level bare-hand detector is integrated with a 58 keyboard characters recognition model. At first, the Gaussian mixture model (GMM) based foreground detector is used to segment the region of interest (ROI), which is further classified using Color-texture and texture based models to detect the actual fist. The detected hand is tracked using modified Kanade–Lucas–Tomasi (KLT) tracker to generate the required trajectory points of the character. The feature space for character recognition consists of existing features and three new features, namely, Local Geometrical Area Ratio (LGAR), Area of two halves (ATH), Curve-Area feature (CAF) that are extracted from the trajectory points. Feature space is optimized using statistical analysis algorithms. Multi-factor analysis of individual character subsets such as alphabets, numbers, ASCII characters, etc., are carried out using multiple conventional classifiers along with Support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and proposed Neuro-fuzzy classifiers. The proposed GMM based motion detection method achieves an accuracy of 100% during the segmentation of ROI, followed by an increase of 46.77% in the accuracy of two-level hand detection under non-ideal conditions. Maximum accuracy of 58 character system using proposed features and ANN classifier is observed to be 92.56%.

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9.
In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD) using soft computing techniques is presented. As the biomedical images such as fundus images are often acquired in high resolution, the Region of Interest (ROI) for glaucoma diagnosis must be selected at first to reduce the complexity of any system. The DMGD system uses a series of pre-processing; initial cropping by the green channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), blood vessel detection and removal by Gaussian Derivative Filters (GDF) and inpainting algorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequency domain features from LAWS are generated from the corresponding ROI for further classification using kernel based Support Vector Machine (SVM). The DMGD system performances are validated using four fundus image databases; ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels; Linear Kernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel, Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernel based classifies from the properly segmented ROI.  相似文献   

10.
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