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Monocular Pedestrian Detection: Survey and Experiments
, 2008
"... Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspective. The first ..."
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Cited by 153 (13 self)
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Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspective. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of stateoftheart systems: waveletbased AdaBoost cascade [74], HOG/linSVM [11], NN/LRF [75] and combined shapetexture detection [23]. Experiments are performed on an extensive dataset captured onboard a vehicle driving through urban environment. The dataset includes many thousands of training samples as well as a 27 minute test sequence involving more than 20000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the waveletbased AdaBoost cascade approach at lower image resolutions and (near) realtime processing speeds. The dataset (8.5GB) is made public for benchmarking purposes.
Simultaneous feature selection and clustering using mixture models
 IEEE TRANS. PATTERN ANAL. MACH. INTELL
, 2004
"... Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched u ..."
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Cited by 122 (1 self)
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Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectationmaximization (EM) algorithm to estimate it, in the context of mixturebased clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters.
A local search approximation algorithm for kmeans clustering
, 2004
"... In kmeans clustering we are given a set of n data points in ddimensional space ℜd and an integer k, and the problem is to determine a set of k points in ℜd, called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomialtime algorithms are kno ..."
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Cited by 113 (1 self)
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In kmeans clustering we are given a set of n data points in ddimensional space ℜd and an integer k, and the problem is to determine a set of k points in ℜd, called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomialtime algorithms are known for this problem. Although asymptotically efficient approximation algorithms exist, these algorithms are not practical due to the very high constant factors involved. There are many heuristics that are used in practice, but we know of no bounds on their performance. We consider the question of whether there exists a simple and practical approximation algorithm for kmeans clustering. We present a local improvement heuristic based on swapping centers in and out. We prove that this yields a (9 + ε)approximation algorithm. We present an example showing that any approach based on performing a fixed number of swaps achieves an approximation factor of at least (9 − ε) in all sufficiently high dimensions. Thus, our approximation factor is almost tight for algorithms based on performing a fixed number of swaps. To establish the practical value of the heuristic, we present an empirical study that shows that, when combined with
Audiovisual automatic speech recognition: An overview.
 In Issues in Visual and AudioVisual Speech Processing.
, 2004
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Modeling of Indoor Positioning Systems Based on Location Fingerprinting
, 2004
"... In recent years, positioning systems for indoor areas using the existing wireless local area network infrastructure have been suggested. Such systems make use of location fingerprinting rather than time or direction of arrival techniques for determining the location of mobile stations. While experim ..."
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Cited by 105 (2 self)
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In recent years, positioning systems for indoor areas using the existing wireless local area network infrastructure have been suggested. Such systems make use of location fingerprinting rather than time or direction of arrival techniques for determining the location of mobile stations. While experimental results related to such positioning systems have been presented, there is a lack of analytical models that can be used as a framework for designing and deploying the positioning systems. In this paper, we present an analytical model for analyzing such positioning systems. We develop the framework for analyzing a simple positioning system that employs the Euclidean distance between a sample signal vector and the location fingerprints of an area stored in a database. We analyze the effect of the number of access points that are visible and radio propagation parameters on the performance of the positioning system and provide some preliminary guidelines on its design.
A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video
, 2002
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Linear dimensionality reduction via a heteroscedastic extension of lda: The chernoff criterion
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—We propose an eigenvectorbased heteroscedastic linear dimension reduction (LDR) technique for multiclass data. The technique is based on a heteroscedastic twoclass technique which utilizes the socalled Chernoff criterion, and successfully extends the wellknown linear discriminant analys ..."
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Cited by 95 (0 self)
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Abstract—We propose an eigenvectorbased heteroscedastic linear dimension reduction (LDR) technique for multiclass data. The technique is based on a heteroscedastic twoclass technique which utilizes the socalled Chernoff criterion, and successfully extends the wellknown linear discriminant analysis (LDA). The latter, which is based on the Fisher criterion, is incapable of dealing with heteroscedastic data in a proper way. For the twoclass case, the betweenclass scatter is generalized so to capture differences in (co)variances. It is shown that the classical notion of betweenclass scatter can be associated with Euclidean distances between class means. From this viewpoint, the betweenclass scatter is generalized by employing the Chernoff distance measure, leading to our proposed heteroscedastic measure. Finally, using the results from the twoclass case, a multiclass extension of the Chernoff criterion is proposed. This criterion combines separation information present in the class mean as well as the class covariance matrices. Extensive experiments and a comparison with similar dimension reduction techniques are presented. Index Terms—Linear dimension reduction, linear discriminant analysis, Fisher criterion, Chernoff distance, Chernoff criterion. 1
The Combining Classifier: to Train or Not to Train?
"... When more than a single classifier has been trained for the same recognition problem the question arises how this set of classifiers may be combined into a final decision rule. Several fixed combining rules are used that depend on the output values of the base classifiers only. They are almost alway ..."
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Cited by 91 (7 self)
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When more than a single classifier has been trained for the same recognition problem the question arises how this set of classifiers may be combined into a final decision rule. Several fixed combining rules are used that depend on the output values of the base classifiers only. They are almost always suboptimal.
Learning to detect and classify malicious executables in the wild
 Journal of Machine Learning Research
, 2006
"... We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious executables and encoded each as a training example using ngrams of byte codes as features. Such processing resulted in more t ..."
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Cited by 90 (1 self)
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We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious executables and encoded each as a training example using ngrams of byte codes as features. Such processing resulted in more than 255 million distinct ngrams. After selecting the most relevant ngrams for prediction, we evaluated a variety of inductive methods, including naive Bayes, decision trees, support vector machines, and boosting. Ultimately, boosted decision trees outperformed other methods with an area under the ROC curve of 0.996. Results suggest that our methodology will scale to larger collections of executables. We also evaluated how well the methods classified executables based on the function of their payload, such as opening a backdoor and massmailing. Areas under the ROC curve for detecting payload function were in the neighborhood of 0.9, which were smaller than those for the detection task. However, we attribute this drop in performance to fewer training examples and to the challenge of obtaining properly labeled examples, rather than to a failing of the methodology or to some inherent difficulty of the classification task. Finally, we applied detectors to 291 malicious executables discovered after we gathered our original collection, and boosted decision trees achieved a truepositive rate of 0.98 for a desired falsepositive rate of 0.05. This result is particularly important, for it suggests that our methodology could be used as the basis for an operational system for detecting previously undiscovered malicious executables.
Learning to Detect Malicious Executables in the Wild
, 2004
"... In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and encoded each as a training example using ngrams of byte codes as features. Such processing resulted in more than 255 millio ..."
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Cited by 76 (1 self)
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In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and encoded each as a training example using ngrams of byte codes as features. Such processing resulted in more than 255 million distinct ngrams. After selecting the most relevant ngrams for prediction, we evaluated a variety of inductive methods, including naive Bayes, decision trees, support vector machines, and boosting. Ultimately, boosted decision trees outperformed other methods with an area under the roc curve of 0.996. Results also suggest that our methodology will scale to larger collections of executables. To the best of our knowledge, ours is the only fielded application for this task developed using techniques from machine learning and data mining.