nils-herrmann/clean_scirepeval huggingface dataset:
Class-Specific Reference Discriminant Analysis With Application in Human Behavior Analysis

In this paper, a novel nonlinear subspace learning technique for class-specific data representation is proposed. A novel data representation is obtained by applying nonlinear class-specific data projection to a discriminant feature space, where the data belonging to the class under consideration are enforced to be close to their class representation, while the data belonging to the remaining classes are enforced to be as far as possible from it. A class is represented by an optimized class vector, enhancing class discrimination in the resulting feature space. An iterative optimization scheme is proposed to this end, where both the optimal nonlinear data projection and the optimal class representation are determined in each optimization step. The proposed approach is tested on three problems relating to human behavior analysis: Face recognition, facial expression recognition, and human action recognition. Experimental results denote the effectiveness of the proposed approach, since the proposed class-specific reference discriminant analysis outperforms kernel discriminant analysis, kernel spectral regression, and class-specific kernel discriminant analysis, as well as support vector machine-based classification, in most cases.
Facial expression recognition using clustering discriminant Non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) is among the most popular subspace methods widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis criteria and achieves an efficient decomposition of the provided data to its discriminant parts has been proposed. However, this approach poses several limitations since it assumes that the underline data distribution forms compact sets which is often unrealistic. To remedy this limitation we regard that data inside each class form various number of clusters and apply a Clustering based Discriminant Analysis. The proposed method combines appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space. Experimental results performed on the Cohn-Kanade database verified the effectiveness of the proposed method in the facial expression recognition task.
Exploiting the SVM constraints in NMF with application in eating and drinking activity recognition

A novel method is introduced for exploiting the support vector machine constraints in nonnegative matrix factorization. The notion of the proposed method is to find the projection matrix that projects the data to a low-dimensional space so that the data projections between the two classes are separated with maximum margin. Experiments were performed for the task of eating and drinking activity classification. Experimental results showed that the proposed method achieves better classification performance than the state of the art nonnegative matrix factorization and discriminant nonnegative matrix factorization followed by support vector machines classification.
Person Identity Label Propagation in Stereo Videos

In this paper a novel method is introduced for propagating person identity labels on facial images extracted from stereo videos. It operates on image data with multiple representations and calculates a projection matrix that preserves locality information and a priori pairwise information, in the form of must-link and cannot-link constraints between the various data representations. The final data representation is a linear combination of the projections of all data representations. Moreover, the proposed method takes into account information obtained through data clustering. This information is exploited during the data propagation step in two ways: to regulate the similarity strength between the projected data and to indicate which samples should be selected for label propagation initialization. The performance of the proposed Multiple Locality Preserving Projections with Cluster-based Label Propagation (MLPP-CLP) method was evaluated on facial images extracted from stereo movies. Experimental results showed that the proposed method outperforms state of the art methods.
A Novel Kernel Discriminant Analysis for Face Verification

In this paper a novel non-linear subspace method for face verification is proposed. The problem of face verification is considered as a two-class problem (genuine versus impostor class). The typical Fisher's linear discriminant analysis (FLDA) gives only one or two projections in a two-class problem. This is a very strict limitation to the search of discriminant dimensions. As for the FLDA for N class problems (N is greater than two) the transformation is not person specific. In order to remedy these limitations of FLDA, exploit the individuality of human faces and take into consideration the fact that the distribution of facial images, under different viewpoints, illumination variations and facial expression is highly complex and non-linear, novel kernel discriminant algorithms are proposed. The new methods are tested in the face verification problem using the XM2VTS database where it is verified that they outperform other commonly used kernel approaches.
Neurons With Paraboloid Decision Boundaries for Improved Neural Network Classification Performance

In mathematical terms, an artificial neuron computes the inner product of a <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensional input vector <inline-formula> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula> with its weight vector <inline-formula> <tex-math notation="LaTeX">${w}$ </tex-math></inline-formula>, compares it with a bias value <inline-formula> <tex-math notation="LaTeX">$w_{0}$ </tex-math></inline-formula> and fires based on the result of this comparison. Therefore, its decision boundary is given by the equation <inline-formula> <tex-math notation="LaTeX">${w}^{T}{x}+w_{0}=0$ </tex-math></inline-formula>. In this paper, we propose replacing the linear hyperplane decision boundary of a neuron with a curved, paraboloid decision boundary. Thus, the decision boundary of the proposed paraboloid neuron is given by the equation <inline-formula> <tex-math notation="LaTeX">$({h}^{T}{x}+h_{0})^{2}-||{x}-{p}||_{2}^{2}=0$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$h_{0}$ </tex-math></inline-formula> denote the parameters of the directrix and <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> denotes the coordinates of the focus. Such paraboloid neural networks are proven to have superior recognition accuracy in a number of applications.
Face verification based on morphological shape decomposition

Morphological shape decomposition is used to model a facial image region as a sum of components and to extract a feature vector at the nodes of a sparse grid overlaid over the facial area in dynamic link matching. The feature vector is comprised of the greylevel values at this node in the reconstructed images at several decomposition levels. This feature vector is subsequently employed in Dynamic Link Architecture to verify the identity of each person from a training set. The experimental results indicate that the proposed combination of morphological shape decomposition and dynamic link matching practically offers the same verification capability to the standard dynamic link matching with Gabor wavelets.
Human action recognition based on bag of features and multi-view neural networks

In this paper, we employ Single-hidden Layer Feedforward Neural networks in order to perform human action recognition based on multiple action representations. In order to determine both optimized network and action representation combination weights, we propose an optimization process that jointly minimizes the overall network training error and the within-class variance of the training data in the corresponding hidden layer spaces. The proposed approach has been evaluated by using the state-of-the-art Bag of Features-based action video representation on three publicly available action recognition databases, where it outperforms two commonly used video representation combination approaches, as well as the best single-descriptor classification outcome.
Kernel matrix trimming for improved Kernel K-means clustering

The Kernel k-Means algorithm for clustering extends the classic k-Means clustering algorithm. It uses the kernel trick to implicitly calculate distances on a higher dimensional space, thus overcoming the classic algorithm's inability to handle data that are not linearly separable. Given a set of n elements to cluster, the n × n kernel matrix is calculated, which contains the dot products in the higher dimensional space of every possible combination of two elements. This matrix is then referenced to calculate the distance between an element and a cluster center, as per classic k-Means. In this paper, we propose a novel algorithm for zeroing elements of the kernel matrix, thus trimming the matrix, which results in reduced memory complexity and improved clustering performance.
Comparison of face verification results on the XM2VTFS database

Presents results of the face verification contest that was organized in conjunction with International Conference on Pattern Recognition 2000. Participants had to use identical data sets from a large, publicly available multimodal database XM2VTSDB. Training and evaluation was carried out according to an a priori known protocol. Verification results of all tested algorithms have been collected and made public on the XM2VTSDB website, facilitating large scale experiments on classifier combination and fusion. Tested methods included, among others, representatives of the most common approaches to face verification -elastic graph matching, Fisher's linear discriminant and support vector machines.
Efficient Camera Control using 2D Visual Information for Unmanned Aerial Vehicle-based Cinematography

Using Unmanned Aerial Vehicles (UAVs), also known as drones, for covering public sport events, such as bicycle races, is becoming increasingly popular. Even though the problem of controlling the flight path of a drone is well studied in the literature, little work has been done on controlling the shooting camera for producing professional grade video footage. In this work we propose a fast and efficient proportional-integral-derivative (PID) based control algorithm that rely solely on 2D visual information and we demonstrate that it is possible to accurately control the camera without inferring the 3D position of the target. To ensure that the proposed method will not exhibit undesired behavior, a genetic algorithm is used to tune its parameters using a properly defined fitness function. The proposed method is evaluated using two datasets that contain actual drone footage: a dataset that contains videos of a single cyclist, and a dataset that contains actually footage from a bicycle race event, the Giro D'Italia bicycle race.
Face Verification using Locally Linear Discriminant Models

When linear discriminant analysis (LDA) is employed, the correct classification of a sample heavily depends on having an adequately large training set. This is often not possible in practical applications, such as person verification, where the lack of sufficient training samples causes improper estimation of a linear separation hyper-plane between the two classes. To overcome this shortcoming a novel algorithm that can handle the verification problem more efficiently than traditional LDA is presented. The dimensionality of the samples is reduced by breaking them down, thus creating subsets of smaller dimensionality feature vectors, and applying discriminant analysis on each subset. The resulting discriminant weight sets are themselves weighted under a normalization criterion, making the discriminant functions continuous in this sense. A series of simulations that formulate the face verification problem illustrate the cases for which our method outperforms traditional LDA and various statistical observations are made about the discriminant coefficients that are generated.
Combining Fuzzy Vector Quantization With Linear Discriminant Analysis for Continuous Human Movement Recognition

In this paper, a novel method for continuous human movement recognition based on fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) is proposed. We regard a movement as a unique combination of basic movement patterns, the so-called dynemes. The proposed algorithm combines FVQ and LDA to discover the most discriminative dynemes as well as represent and discriminate the different human movements in terms of these dynemes. This method allows for simple Mahalanobis or cosine distance comparison of not aligned human movements, taking into account implicitly time shifts and internal speed variations, and, thus, aiding the design of a real-time continuous human movement recognition algorithm. The effectiveness and robustness of this method is shown by experimental results on a standard dataset with videos captured under real conditions, and on a new video dataset created using motion capture data.
Eating and drinking activity recognition based on discriminant analysis of fuzzy distances and activity volumes

Eating and drinking activity recognition can be considered a solitary research field in activity recognition area. The development of an application capable to identify human eating and drinking activity can be really useful in a smart home environment targeting to extend independent living of older persons in the early stages of dementia. In this paper a novel method aiming at eating and drinking activity recognition is presented. Activities are considered as a sequence of human body poses forming 3D volumes, in which the third dimension refers to time. Fuzzy Vector Quantization is performed to associate the 3D volume representation of an activity video with 3D volume prototypes and Linear Discriminant Analysis is used to map activity representations in a low dimensional discriminant feature space. In this space a simple Nearest Centroid classification procedure leads to very satisfactory classification results.
Discriminant Graph Structures for Face Verification

Elastic graph matching is one of the most well known techniques for frontal face recognition/verification and one of the few techniques that can be combined successfully with fully automatic face localization and alignment methods. In this paper, we propose an algorithm for finding the most discriminant features upon a person's face and a person-specific graph is placed in the spatial coordinates that correspond to these discriminant features. We illustrate the improvements in performance by applying the proposed method in frontal face verification using the XM2VTS database.
Multi-view Human Action Recognition: A Survey

While single-view human action recognition has attracted considerable research study in the last three decades, multi-view action recognition is, still, a less exploited field. This paper provides a comprehensive survey of multi-view human action recognition approaches. The approaches are reviewed following an application-based categorization: methods are categorized based on their ability to operate using a fixed or an arbitrary number of cameras. Finally, benchmark databases frequently used for evaluation of multi-view approaches are briefly described.
Person identification from actions based on Artificial Neural Networks

In this paper, we propose a person identification method exploiting human motion information. A Self Organizing Neural Network is employed in order to determine a topographic map of representative human body poses. Fuzzy Vector Quantization is applied to the human body poses appearing in a video in order to obtain a compact video representation, that will be used for person identification and action recognition. Two feedforward Artificial Neural Networks are trained to recognize the person ID and action class labels of a given test action video. Network outputs combination, based on another feedforward network, is performed in the case of multiple cameras used in the training and identification phases. Experimental results on two publicly available databases evaluate the performance of the proposed person identification approach.
On the Optimal Class Representation in Linear Discriminant Analysis

Linear discriminant analysis (LDA) is a widely used technique for supervised feature extraction and dimensionality reduction. LDA determines an optimal discriminant space for linear data projection based on certain assumptions, e.g., on using normal distributions for each class and employing class representation by the mean class vectors. However, there might be other vectors that can represent each class, to increase class discrimination. In this brief, we propose an optimization scheme aiming at the optimal class representation, in terms of Fisher ratio maximization, for LDA-based data projection. Compared with the standard LDA approach, the proposed optimization scheme increases class discrimination in the reduced dimensionality space and achieves higher classification rates in publicly available data sets.
Watermarking of 3D models using principal component analysis

A novel method for 3D model watermarking robust to geometric distortions such as rotation, translation and scaling is proposed. A ternary watermark is embedded in the vertex topology of a 3D model. A transformation of the model to an invariant space is proposed prior to watermark embedding. Simulation results indicate the ability of the proposed method to deal with the aforementioned attacks giving very good results.
Shot type characterization in 2D and 3D video content

Due to the enormous increase of video and image content on the web in the last decades, automatic video annotation became a necessity. The successful annotation of video and image content facilitate a successful indexing and retrieval in search databases. In this work we study a variety of possible shot type characterizations that can be assigned in a single video frame or still image. Possible ways to propagate these characterizations to a video segment (or to an entire shot) are also discussed. A method for the detection of Over-the-Shoulder shots in 3D (stereo) video is also proposed.
Minimum Variance Extreme Learning Machine for human action recognition

In this paper we propose an algorithm for Single-hidden Layer Feedforward Neural networks training. Based on the observation that the learning process of such networks can be considered to be a non-linear mapping of the training data to a high-dimensional feature space, followed by a data projection process to a low-dimensional space where classification is performed by a linear classifier, we extend the Extreme Learning Machine (ELM) algorithm in order to exploit the training data dispersion in its optimization process. The proposed Minimum Variance Extreme Learning Machine classifier is evaluated in human action recognition, where we compare its performance with that of other ELM-based classifiers, as well as the kernel Support Vector Machine classifier.
Using support vector machines for face authentication based on elastic graph matching

A novel method for enhancing the performance of elastic graph matching in face authentication is proposed. Our objective is to weigh the local matching errors at the nodes of an elastic graph according to their discriminatory power. We propose a novel approach to discriminant analysis that re-formulates Fisher's linear discriminant ratio to a quadratic optimization problem subject to inequality constraints by combining statistical pattern recognition and support vector machines. The method is applied to frontal face authentication on the M2VTS database.
Active classification for human action recognition

In this paper, we propose a novel classification method involving two processing steps. Given a test sample, the training data residing to its neighborhood are determined. Classification is performed by a Single-hidden Layer Feedforward Neural network exploiting labeling information of the training data appearing in the test sample neighborhood and using the rest training data as unlabeled. By following this approach, the proposed classification method focuses the classification problem on the training data that are more similar to the test sample under consideration and exploits information concerning to the training set structure. Compared to both static classification exploiting all the available training data and dynamic classification involving data selection for classification, the proposed active classification method provides enhanced classification performance in two publicly available action recognition databases.
Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification

In this paper, two supervised methods for enhancing the classification accuracy of the Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the NMF algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The first method employs discriminant analysis in the features derived from NMF. In this way, a two-phase discriminant feature extraction procedure is implemented, namely NMF plus Linear Discriminant Analysis (LDA). The second method incorporates the discriminant constraints inside the NMF decomposition. Thus, a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived. The introduced methods have been applied to the problem of frontal face verification using the well-known XM2VTS database. Both methods greatly enhance the performance of NMF for frontal face verification
Semi-supervised Classification of Human Actions Based on Neural Networks

In this paper, we propose a novel algorithm for Single-hidden Layer Feed forward Neural networks training which is able to exploit information coming from both labeled and unlabeled data for semi-supervised action classification. We extend the Extreme Learning Machine algorithm by incorporating appropriate regularization terms describing geometric properties and discrimination criteria of the training data representation in the ELM space to this end. The proposed algorithm is evaluated on human action recognition, where its performance is compared with that of other (semi-)supervised classification schemes. Experimental results on two publicly available action recognition databases denote its effectiveness.
Time-series classification using neural Bag-of-Features

Classification of time-series data is a challenging problem with many real-world applications, ranging from identifying medical conditions from electroencephalography (EEG) measurements to forecasting the stock market. The well known Bag-of-Features (BoF) model was recently adapted towards time-series representation. In this work, a neural generalization of the BoF model, composed of an RBF layer and an accumulation layer, is proposed as a neural layer that receives the features extracted from a time-series and gradually builds its representation. The proposed method can be combined with any other layer or classifier, such as fully connected layers or feature transformation layers, to form deep neural networks for time-series classification. The resulting networks are end-to-end differentiable and they can be trained using regular back-propagation. It is demonstrated, using two time-series datasets, including a large-scale financial dataset, that the proposed approach can significantly increase the classification metrics over other baseline and state-of-the-art techniques.