nils-herrmann/clean_scirepeval huggingface dataset:
Kernel subclass support vector description for face and human action recognition

In this paper, we present the Kernel Subclass Support Vector Data Description classifier. We focus on face recognition and human action recognition applications, where we argue that sub-classes are formed within the training class. We modify the standard SVDD optimization problem, so that it exploits subclass information in its optimization process. We extend the proposed method to work in feature spaces of arbitrary dimensionality. We evaluate the proposed method in publicly available face recognition and human action recognition datasets. Experimental results have shown that increased performance can be obtained by employing the proposed method.
Class-Specific Kernel-Discriminant Analysis for Face Verification

In this paper, novel nonlinear subspace methods for face verification are 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 nonlinear, novel kernel-discriminant algorithms are proposed. The new methods are tested in the face verification problem using the XM2VTS, AR, ORL, Yale, and UMIST databases where it is verified that they outperform other commonly used kernel approaches such as kernel-PCA (KPCA), kernel direct discriminant analysis (KDDA), complete kernel Fisher's discriminant analysis (CKFDA), the two-class KDDA, CKFDA, and other two-class and multiclass variants of kernel-discriminant analysis based on Fisher's criterion.
Exploiting supervised learning for finetuning deep CNNs in content based image retrieval

In this paper a novel CNN-based approach in the Content Based Image Retrieval domain that exploits supervised learning is proposed. We employ a deep CNN model to derive feature representations from the activations of the deepest layers and we refine the weights of the utilized layers in order to produce better image descriptors using information obtained from the available data labels. To this end, we adapt the pretrained model and we retrain it on the dataset so that each image representation comes closer in terms of Euclidean distance to its nearest relevant representations and moves away from the irrelevant ones. Experimental results on four publicly available datasets for image retrieval denote the effectiveness of the proposed method in enhancing the retrieval performance, outperforming other CNN-based retrieval techniques in three out of four datasets, as well as traditional handcrafted approaches.
Laplacian Support Vector Analysis for Subspace Discriminative Learning

In this paper we propose a novel dimensionality reduction method that is based on successive Laplacian SVM projections in orthogonal deflated subspaces. The proposed method, called Laplacian Support Vector Analysis, produces projection vectors, which capture the discriminant information that lies in the subspace orthogonal to the standard Laplacian SVMs. We show that the optimal vectors on these deflated subspaces can be computed by successively training a standard SVM with specially designed deflation kernels. The resulting normal vectors contain discriminative information that can be used for feature extraction. In our analysis, we derive an explicit form for the deflation matrix of the mapped features in both the initial and the Hilbert space by using the kernel trick and thus, we can handle linear and non-linear deflation transformations. Experimental results in several benchmark datasets illustrate the strength of our proposed algorithm.
Discriminant NMFfaces for Frontal Face Verification

In this paper, a novel supervised feature extraction method is presented. The method employs discriminant analysis in the features derived by non-negative matrix factorization (NMF). In this way, a two phase discriminant feature extraction procedure is implemented, namely NMF plus linear discriminant analysis (LDA). The introduced method has been applied to the problem of frontal face verification using the well known XM2VTS database, where a better performance than NMF, eigenfaces and Fisherfaces has been achieved
Merging linear discriminant analysis with Bag of Words model for human action recognition

In this paper we propose a novel method for human action recognition, that unifies discriminative Bag of Words (BoW)-based video representation and discriminant subspace learning. An iterative optimization scheme is proposed for sequential discriminant BoWs-based action representation and code-book adaptation based on action discrimination in a reduced dimensionality feature space where action classes are better discriminated. Experiments on four publicly available action recognition data sets demonstrate that the proposed unified approach increases the discriminative ability of the obtained video representation, providing enhanced action classification performance.
Methods for improving discriminant analysis for face authentication

A novel algorithm that can be used to boost the performance of face authentication methods that utilize Fisher's criterion is presented. The algorithm is applied to matching error data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of locally linear discriminant models is used in order to calculate discriminant weights in a more accurate way than the traditional linear discriminant analysis (LDA) methodology. Additionally, defective discriminant coefficients are identified and reestimated. The second phase defines proper combinations for person-specific matching scores and describes an outlier removal process that enhances the classification ability. Our technique was tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the authentication algorithm's performance.
Advances in Elastic Graph Matching for Frontal 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 a series of techniques that enhance the performance of elastic graph matching in frontal face verification by exploiting the individuality of human facial features in many ways. First the use of discriminant analysis in the feature vectors of the graph nodes is explored. The use of the node deformation for discrimination is also proposed. Moreover, the local similarity values at the nodes of the elastic graph, are weighted by coefficients that are also derived from some discriminant analysis in order to form a total similarity measure between faces. We present an algorithm that combines all the above discriminant steps. Moreover, we propose an algorithm for finding the most discriminant landmarks 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 the proposed advances in frontal face verification using the XM2VTS database
Video characterization based on activity clustering

In this paper, we propose a method for video characterization based on activity description information. We employ a state-of-the-art video representation in order to learn human activity concepts, i.e., video groups formed by videos depicting similar human activities. In order to exploit the enriched visual information that is available in multi-view settings, we propose the use of the circular shift invariance property of the coefficients of the Discrete Fourier Transform (DFT) that leads to a view-independent multi-view action representation. In the test phase, in order to assign a test video to one (or multiple) activity groups, we perform temporal video segmentation in order to determine shorter videos depicting simple actions. Experimental results on 2 multi-view action databases denote the effectiveness of the proposed approach.
Projected Gradients for Subclass Discriminant Nonnegative Subspace Learning

Current discriminant nonnegative matrix factorization (NMF) methods either do not guarantee convergence to a stationary limit point or assume a compact data distribution inside classes, thus ignoring intra class variance in extracting discriminant data samples representations. To address both limitations, we regard that data inside each class has a multimodal distribution, forming various subclasses and perform optimization using a projected gradients framework to ensure limit point stationarity. The proposed method combines appropriate clustering-based discriminant criteria in the NMF decomposition cost function, in order to find discriminant projections that enhance class separability in the reduced dimensional projection space, thus improving classification performance. The developed algorithms have been applied to facial expression, face and object recognition, and experimental results verified that they successfully identified discriminant parts, thus enhancing recognition performance.
Facial expression recognition with robust covariance estimation and Support Vector Machines

In this paper, a new framework for facial expression recognition is presented. A Support Vector Machine (SVM) variant is proposed, which makes use of robust statistics. We investigate the use of statistically robust location and dispersion estimators, in order to enhance the performance of a facial expression recognition algorithm by using the support vector machines. The efficiency of the proposed method is tested for two-class and multi-class classification problems. In addition to the experiments conducted in facial expression database we also conducted experiments on classification databases to provide evidence that our method outperforms state of the art methods.
Image authentication using chaotic mixing systems

A novel method for image authentication is proposed. A watermark signal is embedded in a grayscale or a color host image. The watermark key controls a set of parameters of a chaotic system used for the watermark generation. The use of chaotic mixing increases the security of the proposed method and provides the additional feature of imperceptible encryption of the image owner logo in the host image. The method succeeds in detecting any alteration made in a watermarked image. The proposed method is robust in high quality lossy image compression. It provides the user not only with a measure for the authenticity of the test image but also with an image map that highlights the unaltered image regions when selective tampering has been made.
Morphological elastic graph matching applied to frontal face authentication under optimal and real conditions

In this paper, morphological elastic graph matching is applied to frontal face authentication on databases collected either under optimal conditions or during real-world tests (i.e., access-control to buildings or tele-services via Internet in a typical office environment). It is demonstrated that the morphological elastic graph matching achieves a very low equal error rate on databases collected under optimal conditions. However its performance deteriorates in real-world experiments. The compensation for variable recording conditions, such as changes in illumination, scale differences and varying face position prior to the application of morphological elastic matching is proposed. The results obtained indicate that the proposed approach overcomes the image variations and stabilizes the performance of the authentication algorithm.
Face recognition via adaptive discriminant clustering

This paper presents a methodology that tackles the face recognition problem by accommodating multiple clustering steps. At each clustering step, the test and training faces are projected to a discriminant space and the projected training data are partitioned into clusters using the k-means algorithm. Then a subset of the training data clusters is selected, based on how similar the faces in these clusters are to the test face. In the clustering step that follows a new discriminant space is defined by processing this subset and both the test and training data are projected to this space. This process is repeated until one final cluster is selected and the most similar, to the test face, face class contained is set as the identity match. The UMIST and XM2VTS face databases have been used to evaluate the algorithm and results indicate that the proposed framework provides a promising solution to the face recognition problem.
Visual Object Tracking Based on Local Steering Kernels and Color Histograms

In this paper, we propose a visual object tracking framework, which employs an appearance-based representation of the target object, based on local steering kernel descriptors and color histogram information. This framework takes as input the region of the target object in the previous video frame and a stored instance of the target object, and tries to localize the object in the current frame by finding the frame region that best resembles the input. As the object view changes over time, the object model is updated, hence incorporating these changes. Color histogram similarity between the detected object and the surrounding background is employed for background subtraction. Experiments are conducted to test the performance of the proposed framework under various conditions. The proposed tracking scheme is proven to be successful in tracking objects under scale and rotation variations and partial occlusion, as well as in tracking rather slowly deformable articulated objects.
Exploiting local and global geometric data relationships in Support Vector Data Description

In this paper, we describe a one-class classification method based on Support Vector Data Description, which exploits multiple graph structures in its optimization process. We derive in a generic solution which can be employed for supervised one-class classification tasks. The devised method can produce linear or non-linear decision functions, depending on the adopted kernel function. In our experiments, we simultaneously adopted two graphs that describe local and global geometric training data relationships, respectively. We evaluated the proposed classifier in publicly available datasets, where its performance compared favorably against closely related methods.
Enhanced similarities for a music Recommender System

Music is an essential part of human life. It is a way to express ourselves. Its importance gave rise to music Recommender Systems (RS) through e-commerce applications. The most commonly used technique in such applications is Collaborative Filtering (CF) that uses the User-Item (UI) matrix. The latter codes the users' preferences for items. The advantage of using CF methods is their simplicity and their relatively high efficiency. However, data sparsity deteriorates their efficiency. In this paper, we propose a process that can enhance the similarities among users in a way that can address the sparsity problem. The proposed user based CF algorithm alters the values of similarity matrix between users by incorporating a graph based method, improving the performance of the RS. We also propose a method that groups users using spectral clustering and together with the graph-based similarity method yield even more accurate predictions. Experiments have been conducted on a music dataset, highlighting the superiority of the proposed method against typical user-based CF.
Using Support Vector Machines to Enhance the Performance of Elastic Graph Matching for Frontal Face Authentication

A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point is to weigh the local similarity values at the nodes of an elastic graph according to their discriminatory power. Powerful and well-established optimization techniques are used to derive the weights of the linear combination. More specifically, we propose a novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and support vector machines (SVM). Both linear and nonlinear SVM are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces, respectively. The method has been applied to frontal face authentication on the M2VTS database. Experimental results indicate that the performance of morphological elastic graph matching is highly improved by using the proposed weighting technique.
Bernoulli shift generated chaotic watermarks: theoretic investigation

The paper statistically analyzes the behaviour of chaotic watermark signals generated by n-way Bernoulli shift maps. For this purpose, a simple blind copyright protection watermarking system is considered. The analysis involves theoretical evaluation of the system detection reliability, when a correlator detector is used. The aim of the paper is twofold: (i) to introduce the n-way Bernoulli shift generated chaotic watermarks and theoretically contemplate their properties with respect to detection reliability and (ii) to establish theoretically their potential superiority against the widely used pseudorandom watermarks. Experimental verification of the theoretical analysis results is also performed.
A New Class of Decision Surfaces based on the Minimization of within Class Variance

In this paper a modified class of support vector machine (SVM) inspired from the optimization of Fisher's discriminant ratio is presented. The modified class of SVM is used in order to find decision surfaces by solving the corresponding optimization problem in arbitrary Hilbert spaces, defined by Mercer's kernels. The effectiveness of the proposed approach is demonstrated by comparing it with the maximum margin SVM in various experiments using artificial data. Moreover, we have applied the proposed approach in the recognition of neutral expression in facial images.
Fast label propagation on facial images using a pruned similarity matrix

The label propagation process, which is often used to semantically annotate (tag) large amounts of multimedia data assets must be fast, in order to be efficient. In this paper, a novel facial images fast labeling method that is essentially a semi-supervised face recognition approach, is presented. The proposed method is based on the acceleration of a state of the art facial identity label propagation technique. The new method is called pruned label propagation due to the fact that the facial label inference is conducted using a similarity matrix containing fewer entries, namely the pairwise similarities that reside in the main and the off-diagonals of this matrix. Experiments conducted on facial image labeling in three stereoscopic movies, confirm the increased labeling accuracy and the reduced computational complexity of the proposed method.
Frontal face authentication using variants of dynamic link matching based on mathematical morphology

Two variants of dynamic link matching based on mathematical morphology are developed and tested for frontal face authentication, namely, the morphological dynamic link architecture and the morphological signal decomposition-dynamic link architecture. Local coefficients which weigh the contribution of each node in elastic graph matching according to its discriminatory power are derived. The performance of the proposed algorithms is evaluated in terms of their receiver operating characteristic and the equal error rate (EER) achieved in the M2VTS database. The comparison with other frontal face authentication algorithms developed within M2VTS project indicates that morphological dynamic link architecture with discriminatory power coefficients is ranked as the best algorithm in terms of the EER.
Minimum Class Variance Support Vector Machines

In this paper, a modified class of support vector machines (SVMs) inspired from the optimization of Fisher's discriminant ratio is presented, the so-called minimum class variance SVMs (MCVSVMs). The MCVSVMs optimization problem is solved in cases in which the training set contains less samples that the dimensionality of the training vectors using dimensionality reduction through principal component analysis (PCA). Afterward, the MCVSVMs are extended in order to find nonlinear decision surfaces by solving the optimization problem in arbitrary Hilbert spaces defined by Mercer's kernels. In that case, it is shown that, under kernel PCA, the nonlinear optimization problem is transformed into an equivalent linear MCVSVMs problem. The effectiveness of the proposed approach is demonstrated by comparing it with the standard SVMs and other classifiers, like kernel Fisher discriminant analysis in facial image characterization problems like gender determination, eyeglass, and neutral facial expression detection.
Summarization of human activity videos via low-rank approximation

Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic solutions for key-frame extraction. This work presents a method based on selecting as key-frames video frames able to optimally reconstruct the entire video. The novelty lies in modelling the reconstruction algebraically as a Column Subset Selection Problem (CSSP), resulting in extracting key-frames that correspond to elementary visual building blocks. The problem is formulated under an optimization framework and approximately solved via a genetic algorithm. The proposed video summarization method is being evaluated using a publicly available annotated dataset and an objective evaluation metric. According to the quantitative results, it clearly outperforms the typical clustering approach.
Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are well established models capable of achieving state-of-the-art classification accuracy for various computer vision tasks. However, they are becoming increasingly larger, using millions of parameters, while they are restricted to handling images of fixed size. In this paper, a quantization-based approach, inspired from the well-known Bag-of-Features model, is proposed to overcome these limitations. The proposed approach, called Convolutional BoF (CBoF), uses RBF neurons to quantize the information extracted from the convolutional layers and it is able to natively classify images of various sizes as well as to significantly reduce the number of parameters in the network. In contrast to other global pooling operators and CNN compression techniques the proposed method utilizes a trainable pooling layer that it is end-to-end differentiable, allowing the network to be trained using regular back-propagation and to achieve greater distribution shift invariance than competitive methods. The ability of the proposed method to reduce the parameters of the network and increase the classification accuracy over other state-of-the-art techniques is demonstrated using three image datasets.