1,061 research outputs found
Discriminative Features via Generalized Eigenvectors
Representing examples in a way that is compatible with the underlying
classifier can greatly enhance the performance of a learning system. In this
paper we investigate scalable techniques for inducing discriminative features
by taking advantage of simple second order structure in the data. We focus on
multiclass classification and show that features extracted from the generalized
eigenvectors of the class conditional second moments lead to classifiers with
excellent empirical performance. Moreover, these features have attractive
theoretical properties, such as inducing representations that are invariant to
linear transformations of the input. We evaluate classifiers built from these
features on three different tasks, obtaining state of the art results
A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination
By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks
Novel system of pavement cracking detection algorithms using 1mm 3D surface data
Pavement cracking is one of the major concerns for pavement design and management. There have been rapid developments of automated pavement cracking detection in recent years. However, none of them has been widely accepted so far due to lack of capability of maintaining consistently high detection accuracy for various pavement surfaces. Using 1mm 3D data collected by WayLink Digital Highway Data Vehicle (DHDV), an entire system of algorithms, which consists of Fully Automated Cracking Detection Subsystem, Interactive Cracking Detection Subsystem and Noisy Pattern Detection Subsystem, is proposed in this study for improvements in adaptability, reliability and interactivity of pavement cracking detection.The Fully Automated Cracking Detection Subsystem utilizes 3D Shadow Simulation to find lower areas in local neighborhood, and then eliminates noises by subsequent noise suppressing procedures. The assumption behind 3D Shadow Simulation is that local lower areas will be shadowed under light with a certain projection angle. According to the Precision-Recall Analysis on two real pavement segments, the fully automated subsystem can achieve a high level of Precision and Recall on both pavement segments.The Interactive Cracking Detection Subsystem implements an interactive algorithm proposed in this study, which is capable of improving its detection accuracy by adjustments based on the operator's feedback, to provide a slower but more flexible as well as confident approach to pavement cracking detection. It is demonstrated in the case study that the interactive subsystem can retrieve almost 100 percent of cracks with nearly no noises.The Noisy Pattern Detection Subsystem is proposed to exclude pavement joints and grooves from cracking detection so that false-positive errors on rigid pavements can be reduced significantly. This subsystem applies Support Vector Machines (SVM) to train the classifiers for the recognition of transverse groove, transverse joint, longitudinal groove and longitudinal joint respectively. Based on the trained classifiers, pattern extraction procedures are developed to find the exact locations of pavement joints and grooves.Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is one of multi objective genetic algorithms, is employed in this study to optimize parameters of the fully automated subsystem for the pursuing of high Precision and high Recall simultaneously. In addition to NSGA-II, an Auxiliary Prediction Model (APM) is proposed in this study to assist NSGA-II for faster convergence and better diversity.Finally, CPU-based and GPU-based Parallel Computing Techniques, including MultiGPU, GPU streaming, Multi-Core and Multi-Threading are combined in this study to increase the processing speed for all computational tasks that can be synchronous
Facial emotion recognition using min-max similarity classifier
Recognition of human emotions from the imaging templates is useful in a wide
variety of human-computer interaction and intelligent systems applications.
However, the automatic recognition of facial expressions using image template
matching techniques suffer from the natural variability with facial features
and recording conditions. In spite of the progress achieved in facial emotion
recognition in recent years, the effective and computationally simple feature
selection and classification technique for emotion recognition is still an open
problem. In this paper, we propose an efficient and straightforward facial
emotion recognition algorithm to reduce the problem of inter-class pixel
mismatch during classification. The proposed method includes the application of
pixel normalization to remove intensity offsets followed-up with a Min-Max
metric in a nearest neighbor classifier that is capable of suppressing feature
outliers. The results indicate an improvement of recognition performance from
92.85% to 98.57% for the proposed Min-Max classification method when tested on
JAFFE database. The proposed emotion recognition technique outperforms the
existing template matching methods
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application
Spontaneous subtle emotions are expressed through micro-expressions, which
are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great
challenge for visual recognition. The abrupt but significant dynamics for the
recognition task are temporally sparse while the rest, irrelevant dynamics, are
temporally redundant. In this work, we analyze and enforce sparsity constrains
to learn significant temporal and spectral structures while eliminate
irrelevant facial dynamics of micro-expressions, which would ease the challenge
in the visual recognition of spontaneous subtle emotions. The hypothesis is
confirmed through experimental results of automatic spontaneous subtle emotion
recognition with several sparsity levels on CASME II and SMIC, the only two
publicly available spontaneous subtle emotion databases. The overall
performances of the automatic subtle emotion recognition are boosted when only
significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016
Domain Conditioned Adaptation Network
Tremendous research efforts have been made to thrive deep domain adaptation
(DA) by seeking domain-invariant features. Most existing deep DA models only
focus on aligning feature representations of task-specific layers across
domains while integrating a totally shared convolutional architecture for
source and target. However, we argue that such strongly-shared convolutional
layers might be harmful for domain-specific feature learning when source and
target data distribution differs to a large extent. In this paper, we relax a
shared-convnets assumption made by previous DA methods and propose a Domain
Conditioned Adaptation Network (DCAN), which aims to excite distinct
convolutional channels with a domain conditioned channel attention mechanism.
As a result, the critical low-level domain-dependent knowledge could be
explored appropriately. As far as we know, this is the first work to explore
the domain-wise convolutional channel activation for deep DA networks.
Moreover, to effectively align high-level feature distributions across two
domains, we further deploy domain conditioned feature correction blocks after
task-specific layers, which will explicitly correct the domain discrepancy.
Extensive experiments on three cross-domain benchmarks demonstrate the proposed
approach outperforms existing methods by a large margin, especially on very
tough cross-domain learning tasks.Comment: Accepted by AAAI 202
Margin maximizing discriminant analysis
Abstract. We propose a new feature extraction method called Margin Maximizing Discriminant Analysis (MMDA) which seeks to extract features suitable for classification tasks. MMDA is based on the principle that an ideal feature should convey the maximum information about the class labels and it should depend only on the geometry of the optimal decision boundary and not on those parts of the distribution of the input data that do not participate in shaping this boundary. Further, distinct feature components should convey unrelated information about the data. Two feature extraction methods are proposed for calculating the parameters of such a projection that are shown to yield equivalent results. The kernel mapping idea is used to derive non-linear versions. Experiments with several real-world, publicly available data sets demonstrate that the new method yields competitive results.
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