1,200 research outputs found

    Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

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    We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset

    Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

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    Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance

    Joint cross-domain classification and subspace learning for unsupervised adaptation

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    Domain adaptation aims at adapting the knowledge acquired on a source domain to a new different but related target domain. Several approaches have beenproposed for classification tasks in the unsupervised scenario, where no labeled target data are available. Most of the attention has been dedicated to searching a new domain-invariant representation, leaving the definition of the prediction function to a second stage. Here we propose to learn both jointly. Specifically we learn the source subspace that best matches the target subspace while at the same time minimizing a regularized misclassification loss. We provide an alternating optimization technique based on stochastic sub-gradient descent to solve the learning problem and we demonstrate its performance on several domain adaptation tasks.Comment: Paper is under consideration at Pattern Recognition Letter

    Speaker Identification Based On Discriminative Vector Quantization And Data Fusion

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    Speaker Identification (SI) approaches based on discriminative Vector Quantization (VQ) and data fusion techniques are presented in this dissertation. The SI approaches based on Discriminative VQ (DVQ) proposed in this dissertation are the DVQ for SI (DVQSI), the DVQSI with Unique speech feature vector space segmentation for each speaker pair (DVQSI-U), and the Adaptive DVQSI (ADVQSI) methods. The difference of the probability distributions of the speech feature vector sets from various speakers (or speaker groups) is called the interspeaker variation between speakers (or speaker groups). The interspeaker variation is the measure of template differences between speakers (or speaker groups). All DVQ based techniques presented in this contribution take advantage of the interspeaker variation, which are not exploited in the previous proposed techniques by others that employ traditional VQ for SI (VQSI). All DVQ based techniques have two modes, the training mode and the testing mode. In the training mode, the speech feature vector space is first divided into a number of subspaces based on the interspeaker variations. Then, a discriminative weight is calculated for each subspace of each speaker or speaker pair in the SI group based on the interspeaker variation. The subspaces with higher interspeaker variations play more important roles in SI than the ones with lower interspeaker variations by assigning larger discriminative weights. In the testing mode, discriminative weighted average VQ distortions instead of equally weighted average VQ distortions are used to make the SI decision. The DVQ based techniques lead to higher SI accuracies than VQSI. DVQSI and DVQSI-U techniques consider the interspeaker variation for each speaker pair in the SI group. In DVQSI, speech feature vector space segmentations for all the speaker pairs are exactly the same. However, each speaker pair of DVQSI-U is treated individually in the speech feature vector space segmentation. In both DVQSI and DVQSI-U, the discriminative weights for each speaker pair are calculated by trial and error. The SI accuracies of DVQSI-U are higher than those of DVQSI at the price of much higher computational burden. ADVQSI explores the interspeaker variation between each speaker and all speakers in the SI group. In contrast with DVQSI and DVQSI-U, in ADVQSI, the feature vector space segmentation is for each speaker instead of each speaker pair based on the interspeaker variation between each speaker and all the speakers in the SI group. Also, adaptive techniques are used in the discriminative weights computation for each speaker in ADVQSI. The SI accuracies employing ADVQSI and DVQSI-U are comparable. However, the computational complexity of ADVQSI is much less than that of DVQSI-U. Also, a novel algorithm to convert the raw distortion outputs of template-based SI classifiers into compatible probability measures is proposed in this dissertation. After this conversion, data fusion techniques at the measurement level can be applied to SI. In the proposed technique, stochastic models of the distortion outputs are estimated. Then, the posteriori probabilities of the unknown utterance belonging to each speaker are calculated. Compatible probability measures are assigned based on the posteriori probabilities. The proposed technique leads to better SI performance at the measurement level than existing approaches

    DICTIONARIES AND MANIFOLDS FOR FACE RECOGNITION ACROSS ILLUMINATION, AGING AND QUANTIZATION

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    During the past many decades, many face recognition algorithms have been proposed. The face recognition problem under controlled environment has been well studied and almost solved. However, in unconstrained environments, the performance of face recognition methods could still be significantly affected by factors such as illumination, pose, resolution, occlusion, aging, etc. In this thesis, we look into the problem of face recognition across these variations and quantization. We present a face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose with dictionaries learned for each class. A novel test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. An image relighting technique based on pose-robust albedo estimation is used to generate multiple frontal images of the same person with variable lighting. As a result, the proposed algorithm has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training. The efficiency of the proposed method is demonstrated using publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms. The problem of recognizing facial images across aging remains an open problem. We look into this problem by studying the growth in the facial shapes. Building on recent advances in landmark extraction, and statistical techniques for landmark-based shape analysis, we show that using well-defined shape spaces and its associated geometry, one can obtain significant performance improvements in face verification. Toward this end, we propose to model the facial shapes as points on a Grassmann manifold. The face verification problem is then formulated as a classification problem on this manifold. We then propose a relative craniofacial growth model which is based on the science of craniofacial anthropometry and integrate it with the Grassmann manifold and the SVM classifier. Experiments show that the proposed method is able to mitigate the variations caused by the aging progress and thus effectively improve the performance of open-set face verification across aging. In applications such as document understanding, only binary face images may be available as inputs to a face recognition algorithm. We investigate the effects of quantization on several classical face recognition algorithms. We study the performances of PCA and multiple exemplar discriminant analysis (MEDA) algorithms with quantized images and with binary images modified by distance and Box-Cox transforms. We propose a dictionary-based method for reconstructing the grey scale facial images from the quantized facial images. Two dictionaries with low mutual coherence are learned for the grey scale and quantized training images respectively using a modified KSVD method. A linear transform function between the sparse vectors of quantized images and the sparse vectors of grey scale images is estimated using the training data. In the testing stage, a grey scale image is reconstructed from the quantized image using the transform matrix and normalized dictionaries. The identities of the reconstructed grey scale images are then determined using the dictionary-based face recognition (DFR) algorithm. Experimental results show that the reconstructed images are similar to the original grey-scale images and the performance of face recognition on the quantized images is comparable to the performance on grey scale images. The online social network and social media is growing rapidly. It is interesting to study the impact of social network on computer vision algorithms. We address the problem of automated face recognition on a social network using a loopy belief propagation framework. The proposed approach propagates the identities of faces in photos across social graphs. We characterize its performance in terms of structural properties of the given social network. We propose a distance metric defined using face recognition results for detecting hidden connections. The performance of the proposed method is analyzed on graph structure networks, scalability, different degrees of nodes, labeling errors correction and hidden connections discovery. The result demonstrates that the constraints imposed by the social network have the potential to improve the performance of face recognition methods. The result also shows it is possible to discover hidden connections in a social network based on face recognition

    Orthonormal Product Quantization Network for Scalable Face Image Retrieval

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    Recently, deep hashing with Hamming distance metric has drawn increasing attention for face image retrieval tasks. However, its counterpart deep quantization methods, which learn binary code representations with dictionary-related distance metrics, have seldom been explored for the task. This paper makes the first attempt to integrate product quantization into an end-to-end deep learning framework for face image retrieval. Unlike prior deep quantization methods where the codewords for quantization are learned from data, we propose a novel scheme using predefined orthonormal vectors as codewords, which aims to enhance the quantization informativeness and reduce the codewords' redundancy. To make the most of the discriminative information, we design a tailored loss function that maximizes the identity discriminability in each quantization subspace for both the quantized and the original features. Furthermore, an entropy-based regularization term is imposed to reduce the quantization error. We conduct experiments on three commonly-used datasets under the settings of both single-domain and cross-domain retrieval. It shows that the proposed method outperforms all the compared deep hashing/quantization methods under both settings with significant superiority. The proposed codewords scheme consistently improves both regular model performance and model generalization ability, verifying the importance of codewords' distribution for the quantization quality. Besides, our model's better generalization ability than deep hashing models indicates that it is more suitable for scalable face image retrieval tasks

    Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting

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    This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.Comment: 9 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423, http://sites.google.com/site/ijcsis
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