Comparison of Generative and Discriminative Techniques for Object Detection and Classification

Ulusoy, İlkay; Bishop, Christopher M.

doi:10.1007/11957959_9

Cited by 27 publications

(22 citation statements)

References 13 publications

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“…Using this structure, the negatives will be rejected quickly when they flow through the network while the positives traverse through several branches. By combining the evidence from these branches, we are able to compute p(O|I) exactly using (11).…”

Section: Probabilistic Boosting Network (Pbn)mentioning

confidence: 99%

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Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network

Zhang

Zhou

McMillan

et al. 2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

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In this paper, we present a learning procedure called probabilistic boosting network (PBN) for joint real-time object detection and pose estimation. Grounded on the law of total probability, PBN integrates evidence from two building blocks, namely a multiclass boosting classifier for pose estimation and a boosted detection cascade for object detection. By inferring the pose parameter, we avoid the exhaustive scanning for the pose, which hampers real time requirement. In addition, we only need one integral image/volume with no need of image/volume rotation. We implement PBN using a graph-structured network that alternates the two tasks of foreground/background discrimination and pose estimation for rejecting negatives as quickly as possible. Compared with previous approaches, we gain accuracy in object localization and pose estimation while noticeably reducing the computation. We invoke PBN to detect the left ventricle from a 3D ultrasound volume, processing about 10 volumes per second, and the left atrium from 2D images in real time.

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Section: Probabilistic Boosting Network (Pbn)mentioning

confidence: 99%

“…The above integrating rule is an instance of combining the generative mixture model with discriminative classification models [11]. To train the multiclass classifier and binary object detectors, PBN invokes boosting algorithm to perform feature selection based on a common feature pool.…”

Section: Introductionmentioning

confidence: 99%

Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network

Zhang

Zhou

McMillan

et al. 2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

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show abstract

“…It has shown that discriminative approaches such as SVM [1] outperform model based approaches due to their flexibility in decision boundaries estimation. Conversely, since that discriminative methods are concerned with boundaries, all the classes need to be estimated conjointly [2]. Complementary, one of the interesting characteristics, that generative models have over discriminative ones, is that they are learnt independently for each class.…”

Section: Problem Statementmentioning

confidence: 99%

“…of Theorem 3. 2 We simply need show that such an f 0 satisfies the condition of Lemma 3.2. Let w(x) = exp(−x η ), x ≥ 0.…”

Section: Dirichlet Mixture Of Normalsmentioning

confidence: 99%

A Multi-features Fusion of Multi-temporal Hyperspectral Images using a Cooperative GDD/SVM Method

Hemissi¹,

Farah²

2013

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

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“…In this paper, we give an attention to these aspects. In general, the machine learning classifiers are classified into two categories: generative classifiers and discriminative classifiers . The generative classifiers try to learn the density distribution of a class of data and understand its basic characteristics from the training data, and then find an optimal representation of this class, whereas the discriminative classifiers try to model the boundaries of different classes or to model the class membership probability distribution directly.…”

Section: Introductionmentioning

confidence: 99%

Generative versus discriminative classifiers for android anomaly-based detection system using system calls filtering and abstraction process

Amamra

Robert

Abraham

et al. 2016

Security Comm. Networks

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Anomaly‐based detection techniques have been widely studied in recent years. Most of these efforts have focused to improve the accuracy of these techniques. The poor accuracy performance is caused by two factors: (i) the data used for the analysis is insufficient and/or unrepresentative of the application behavior, or (ii) inappropriate algorithms are used to model the behavior of the application. In this paper, we attempt to improve anomaly‐based detection techniques by examining these two factors. First, we use system call filtering and abstraction process. This process refines the system call traces. The refined traces are compact and should be more representative of the application main behavior. Second, we use machine learning classifiers to characterize the benign behavior. Generally, there are two main categories of machine learning classifiers: generative classifiers and discriminative classifiers. In their initial training phases, the classifiers build models characterizing the benign behavior. Later on, these models are used to distinguish between different classes of data. They are simply defined by their parameters. The k‐means classifier is considered as a representative of the generative classifier category, and the support vector machine classifier as a representative of the discriminative classifier category. The efficiency of these classifiers are reviewed and compared, as well as the impact of the filtering and abstraction process on their performances is evaluated. The experimental results show that the support vector machine model outperforms the k‐means model, and the filtering and abstraction process has positive impacts on the performance of both models. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Comparison of Generative and Discriminative Techniques for Object Detection and Classification

Cited by 27 publications

References 13 publications

Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network

Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network

A Multi-features Fusion of Multi-temporal Hyperspectral Images using a Cooperative GDD/SVM Method

Generative versus discriminative classifiers for android anomaly-based detection system using system calls filtering and abstraction process

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