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32
Traffic sign recognition - How far are we from the solution? IJCNN
, 2013
"... Abstract — Traffic sign recognition has been a recurring appli-cation domain for visual objects detection. The public datasets have only recently reached large enough size and variety to enable proper empirical studies. We revisit the topic by showing how modern methods perform on two large detectio ..."
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Cited by 13 (1 self)
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Abstract — Traffic sign recognition has been a recurring appli-cation domain for visual objects detection. The public datasets have only recently reached large enough size and variety to enable proper empirical studies. We revisit the topic by showing how modern methods perform on two large detection and classification datasets (thousand of images, tens of categories) captured in Belgium and Germany. We show that, without any application specific modification, existing methods for pedestrian detection, and for digit and face classification; can reach performances in the range of 95 % ∼ 99 % of the perfect solution. We show detailed experiments and discuss the trade-off of different options. Our top performing methods use modern vari-ants of HOG features for detection, and sparse representations for classification. I.
Robust Feature Extraction via Information Theoretic Learning
"... In this paper, we present a robust feature extraction framework based on informationtheoretic learning. Its formulated objective aims at simultaneously maximizing the Renyi’s quadratic information potential of features and the Renyi’s cross information potential between features and class labels. Th ..."
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Cited by 11 (5 self)
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In this paper, we present a robust feature extraction framework based on informationtheoretic learning. Its formulated objective aims at simultaneously maximizing the Renyi’s quadratic information potential of features and the Renyi’s cross information potential between features and class labels. This objective function reaps the advantages in robustness from both redescending M-estimator and manifold regularization, and can be efficiently optimized via halfquadratic optimization in an iterative manner. In addition, the popular algorithms LPP, SRDA and LapRLS for feature extraction are all justified to be the special cases within this framework. Extensive comparison experiments on several real-world data sets, with contaminated features or labels, well validate the encouraging gain in algorithmic robustness from this proposed framework. 1.
A Scalable Two-Stage Approach for a Class of Dimensionality Reduction Techniques
"... Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data. Many existing dimensionality reduction techniques can be formulated as a generalized eigenvalue problem, which does not scale to large-size problems. Prior work transforms the generaliz ..."
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Cited by 7 (1 self)
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Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data. Many existing dimensionality reduction techniques can be formulated as a generalized eigenvalue problem, which does not scale to large-size problems. Prior work transforms the generalized eigenvalue problem into an equivalent least squares formulation, which can then be solved efficiently. However, the equivalence relationship only holds under certain assumptions without regularization, which severely limits their applicability in practice. In this paper, an efficient two-stage approach is proposed to solve a class of dimensionality reduction techniques, including Canonical Correlation
Efficient image classification via multiple rank regression
- IEEE Transactions on Image Processing
, 2013
"... Abstract — The problem of image classification has aroused considerable research interest in the field of image processing. Traditional methods often convert an image to a vector and then use a vector-based classifier. In this paper, a novel multiple rank regression model (MRR) for matrix data class ..."
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Cited by 4 (2 self)
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Abstract — The problem of image classification has aroused considerable research interest in the field of image processing. Traditional methods often convert an image to a vector and then use a vector-based classifier. In this paper, a novel multiple rank regression model (MRR) for matrix data classification is proposed. Unlike traditional vector-based methods, we employ multiple-rank left projecting vectors and right projecting vectors to regress each matrix data set to its label for each category. The convergence behavior, initialization, computational complexity, and parameter determination are also analyzed. Compared with vector-based regression methods, MRR achieves higher accuracy and has lower computational complexity. Compared with tradi-tional supervised tensor-based methods, MRR performs better for matrix data classification. Promising experimental results on face, object, and hand-written digit image classification tasks are provided to show the effectiveness of our method. Index Terms — Dimensionality reduction, image classification, multiple rank regression, tensor analysis.
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
"... Abstract. Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn features that account for both types of information based on variational optimization of nonparametric learning c ..."
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Cited by 3 (2 self)
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Abstract. Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn features that account for both types of information based on variational optimization of nonparametric learning criteria. Using mutual information and Bayes error rate as example criteria, we show that high-quality features can be learned from a variational graph embedding procedure, which is solved through an iterative EM-style algorithm where the E-Step learns a variational affinity graph and the M-Step in turn embeds this graph by spectral analysis. The resulting feature learner has several appealing properties such as maximum discrimination, maximum-relevanceminimum-redundancy and locality-preserving. Experiments on benchmark face recognition data sets confirm the effectiveness of our proposed algorithms. 1
Combining Traffic Sign Detection with 3D Tracking Towards Better Driver Assistance,” Emerging topics in computer vision and its applications
, 2011
"... We briefly review the advances in driver assistance systems and present a real-time version that integrates single view detection with region-based 3D tracking of traffic signs. The system has a typical pipeline: detection and recognition of traffic signs in independent frames, followed by tracking ..."
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We briefly review the advances in driver assistance systems and present a real-time version that integrates single view detection with region-based 3D tracking of traffic signs. The system has a typical pipeline: detection and recognition of traffic signs in independent frames, followed by tracking for temporal integration. The detection process finds an optimal set of candidates and is accelerated using AdaBoost cascades. A hierarchy of SVMs handles the recognition of traffic sign types. The 2D detections are then employed in simultaneous 2D segmentation and 3D pose tracking, using the known 3D model of the recognized traffic sign. Thus, we achieve not only 2D tracking of the recognized traffic signs, but we also obtain 3D pose information, which we use to establish the relevance of the traffic sign to the driver. The performance of the system is demonstrated by tracking multiple road signs in real-world scenarios. Traffic signs play a pivotal role in rendering traffic more efficient and safer. Un-fortunately, still many accidents happen because drivers have overlooked a sign.
Effect of Size and Heterogeneity of Samples on Biomarker Discovery: Synthetic and Real Data Assessment
"... Motivation: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputabl ..."
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Cited by 3 (2 self)
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Motivation: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods. Methods: We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state. Results: The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is
Towards Incremental and Large Scale Face Recognition
"... Linear discriminant analysis with nearest neighborhood classifier (LDA + NN) has been commonly used in face recognition, but it often confronts with two problems in real applications: (1) it cannot incrementally deal with the in-formation of training instances; (2) it cannot achieve fast search agai ..."
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Cited by 3 (1 self)
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Linear discriminant analysis with nearest neighborhood classifier (LDA + NN) has been commonly used in face recognition, but it often confronts with two problems in real applications: (1) it cannot incrementally deal with the in-formation of training instances; (2) it cannot achieve fast search against large scale gallery set. In this paper, we use incremental LDA (ILDA) and hashing based search method to deal with these two problems. Firstly two in-cremental LDA algorithms are proposed under spectral re-gression framework, namely exact incremental spectral re-gression discriminant analysis (EI-SRDA) and approximate incremental spectral regression discriminant analysis (AI-SRDA). Secondly we propose a similarity hashing algorithm of sub-linear complexity to achieve quick recognition from large gallery set. Experiments on FRGC and self-collected 100,000 faces database show the effective of our methods. 1.
Locally Regressive Projections
"... Abstract We propose a novel linear dimensionality reduction algorithm, namely Locally Regressive Projections (LRP). To capture the local discriminative structure, for each data point, a local patch consisting of this point and its neighbors is constructed. LRP assumes that the low dimensional repres ..."
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Abstract We propose a novel linear dimensionality reduction algorithm, namely Locally Regressive Projections (LRP). To capture the local discriminative structure, for each data point, a local patch consisting of this point and its neighbors is constructed. LRP assumes that the low dimensional representations of points in each patch can be well estimated by a locally fitted regression function. Specifically, we train a linear function for each patch via ridge regression, and use its fitting error to measure how well the new representations can respect the local structure. The optimal projections are thus obtained by minimizing the summation of the fitting errors over all the local patches. LRP can be performed under either supervised or unsupervised settings. Our theoretical analysis reveals the connections between LRP and the classical methods such as PCA and LDA. Experiments on face recognition and clustering demonstrate the effectiveness of our proposed method. Key words: dimensionality reduction; local learning; locally regressive projections; ridge regression
