nils-herrmann/clean_scirepeval huggingface dataset:
Graph Embedded Extreme Learning Machine

In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed for the calculation of the network's output weights. The proposed graph embedded ELM (GEELM) algorithm is able to naturally exploit both intrinsic and penalty SL criteria that have been (or will be) designed under the graph embedding framework. In addition, we extend the proposed GEELM algorithm in order to be able to exploit SL criteria in arbitrary (even infinite) dimensional ELM spaces. We evaluate the proposed approach on eight standard classification problems and nine publicly available datasets designed for three problems related to human behavior analysis, i.e., the recognition of human face, facial expression, and activity. Experimental results denote the effectiveness of the proposed approach, since it outperforms other ELM-based classification schemes in all the cases.
Compensating for variable recording conditions in frontal face authentication algorithms

This paper addresses the problem of compensating for variable recording conditions such as changes in illumination, scale differences, and varying face position. It is well known that the performance of any face authentication/recognition algorithm deteriorates significantly in the presence of the aforementioned conditions as well as the expression variations. The use of simple and powerful pre-processing techniques aiming at compensating for variable recording conditions prior to the application of any authentication algorithm is proposed. It is shown that such an approach overcomes indeed the image variations and guarantees an almost stable performance for the Morphological Dynamic Link Architecture developed within the European research project M2VTS.
Automated Facial Pose Extraction From Video Sequences Based on Mutual Information

Estimation of the facial pose in video sequences is one of the major issues in many vision systems such as face-based biometrics, scene understanding for humans, and others. The proposed method uses a novel pose estimation algorithm based on mutual information to extract any required facial poses from video sequences. The method extracts the poses automatically and classifies them according to view angle. Experimental results on the XM2VTS video database and on a new database created for the needs of this research indicated a pose classification rate of 99.2% while it was shown that it outperforms a principal component analysis reconstruction method that was used as a benchmark.
Human crowd detection for drone flight safety using convolutional neural networks

In this paper a novel human crowd detection method, that utilizes deep Convolutional Neural Networks (CNN), for drone flight safety purposes is proposed. The aim of our work is to provide light architectures, as imposed by the computational restrictions of the application, that can effectively distinguish between crowded and non-crowded scenes, captured from drones, and provide crowd heatmaps that can be used to semantically enhance the flight maps by defining no-fly zones. To this end, we first propose to adapt a pre-trained CNN on our task, by totally discarding the fully-connected layers and attaching an additional convolutional one, transforming it to a fast fully-convolutional network that is able to produce crowd heatmaps. Second, we propose a two-loss-training model, which aims to enhance the separability of the crowd and non-crowd classes. The experimental validation is performed on a new drone dataset that has been created for the specific task, and indicates the effectiveness of the proposed detector.
Enhancing class discrimination in Kernel Discriminant Analysis

In this paper, we propose an optimization scheme aiming at optimal nonlinear data projection, in terms of Fisher ratio maximization. To this end, we formulate an iterative optimization scheme consisting of two processing steps: optimal data projection calculation and optimal class representation determination. Compared to the standard approach employing the class mean vectors for class representation, the proposed optimization scheme increases class discrimination in the reduced-dimensionality feature space. We evaluate the proposed method in standard classification problems, as well as on the classification of human actions and face, and show that it is able to achieve better generalization performance, when compared to the standard approach.
Discriminant action representation for view-invariant person identification

In this paper we propose a novel person identification method exploiting human motion information. Persons are described by using their poses during action execution. Identification process involves Fuzzy Vector Quantization and Discriminant Learning. In the case of multiple cameras used in the identification phase, single-view identification results combination is achieved by employing a Bayesian combination strategy. The proposed identification approach does not set the assumptions of known action class and number of capturing cameras in the identification phase. Experimental results on two publicly available video databases denote the effectiveness of the proposed approach.
Frontal face authentication using morphological elastic graph matching

A novel dynamic link architecture based on multiscale morphological dilation-erosion is proposed for frontal face authentication. Instead of a set of Gabor filters tuned to different orientations and scales, multiscale morphological operations are employed to yield a feature vector at each node of the reference grid. Linear projection algorithms for feature selection and automatic weighting of the nodes according to their discriminatory power succeed to increase the authentication capability of the method. The performance of the morphological dynamic link architecture is evaluated in terms of the receiver operating characteristic in the M2VTS face image database. The comparison with other frontal face authentication algorithms indicates that the morphological dynamic link architecture with discriminatory power coefficients is the best algorithm with respect to the equal error rate achieved.
Positive and Negative Label Propagations

This paper extends the state-of-the-art label propagation (LP) framework in the propagation of negative labels. More specifically, the state-of-the-art LP methods propagate information of the form “the sample <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> should be assigned the label <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>.” The proposed method extends the state-of-the-art framework by considering additional information of the form “the sample <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> should not be assigned the label <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>.” A theoretical analysis is presented in order to include negative LP in the problem formulation. Moreover, a method for selecting the negative labels in cases when they are not inherent from the data structure is presented. Furthermore, the incorporation of negative label information in two multigraph LP methods is presented. Finally, a discussion on the proposed algorithm extension to out of sample data, as well as scalability issues, is presented. Experimental results in various scenarios showed that the incorporation of negative label information increases, in all cases, the classification accuracy of the state of the art.
Visual Question Answering using Explicit Visual Attention

One of the most complex multi-model problems faced today is Visual Question Answering (VQA), which requires a machine to properly understand a question about a reference visual input, expressed in natural language, and then produce the answer to that question. In order to solve this problem and increase the probability of producing the correct answer, it is crucial to provide reliable attention information. However, existing methods only use implicitly trained attention models that are often unable to attend to the appropriate image region the question refers to, limiting their ability to provide the correct answer. To address this issue, we propose an explicitly trained attention model that is inspired by the theory of pictorial superiority effect. In this model, we use attention-oriented word embeddings that increase the efficiency of learning common representation spaces. The dataset that we use, the Visual7W dataset, is the only dataset that provides visual attention ground truth information. In this paper, we demonstrate the effectiveness of the proposed method over both implicit attention models and other state-of-art VQA techniques.
Deep reinforcement learning for frontal view person shooting using drones

Unmanned Aerial Vehicles (UAVs), also known as drones, are increasingly used for a wide variety of novel tasks, including drone-based cinematography. However, flying drones in such setting requires the coordination of several people, increasing the cost of using drones for aerial cinematography and limiting the shooting flexibility by putting a significant cognitive load on the director and drone/camera operators. To overcome some of these limitation, this paper proposes a deep reinforcement learning (RL) method for performing autonomous frontal view shooting. To this end, a realistic simulation environment is developed, which ensures that the learned agent can be directly deployed on a drone. Then, a deep RL algorithm, tailored to the needs of the specific application, is derived building upon the well known deep Q-learning approach. The effectiveness of the proposed technique is experimentally demonstrated using several quantitative and qualitative experiments.
Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine

In this paper, we propose a scheme that can be used in large-scale nonlinear facial image classification problems. An approximate solution of the kernel Extreme Learning Machine classifier is formulated and evaluated. Experiments on two publicly available facial image datasets using two popular facial image representations illustrate the effectiveness and efficiency of the proposed approach. The proposed Approximate Kernel Extreme Learning Machine classifier is able to scale well in both time and memory, while achieving good generalization performance. Specifically, it is shown that it outperforms the standard ELM approach for the same time and memory requirements. Compared to the original kernel ELM approach, it achieves similar (or better) performance, while scaling well in both time and memory with respect to the training set cardinality.
Enhanced transform-domain correlation-based audio watermarking

Various watermarking techniques have been proposed so far, aiming at the copyright protection of audio signals. Little effort has been made, however, in taking under consideration the spectrum of the watermark sequence itself and exploiting its frequency properties. An enhanced audio watermarking technique, based on correlation detection, is introduced in this paper, where high-frequency chaotic watermarks are multiplicatively embedded in the low frequencies of the DFT domain. A series of experiments have been conducted to demonstrate both detection reliability and robustness against attacks.
Subclass Marginal Fisher Analysis

Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains. Recently, Discriminant Analysis (DA) methods, which use subclass information for the discrimination between the data classes, have attracted much attention. As DA methods are strongly dependent on the underlying distribution of the data, techniques whose functionality is based on neighbourhood information among the data samples have emerged. For instance, based on the Graph Embedding (GE) framework, which is a platform for developing novel DR methods, Marginal Fisher Analysis (MFA) has been proposed. Although MFA surpasses the above distribution limitations, it fails to model potential subclass structure that might lie within the several classes of the data. In this paper, motivated by the need to alleviate the above shortcomings, we propose a novel DR technique, called Subclass Marginal Fisher Analysis (SMFA), which combines the strength of subclass DA methods with the versatility of MFA. The new method is built by extending the GE framework so as to include subclass information. Through a series of experiments on various real-world datasets, it is shown that SMFA outperforms in most of the cases the state-of-the-art demonstrating the potential of exploiting subclass neighbourhood information in the DR process.
Concept detection and face pose estimation using lightweight convolutional neural networks for steering drone video shooting

Unmanned Aerial Vehicles, also known as drones, are becoming increasingly popular for video shooting tasks since they are capable of capturing spectacular aerial shots. Deep learning techniques, such as Convolutional Neural Networks (CNNs), can be utilized to assist various aspects of the flying and the shooting process allowing one human to operate one or more drones at once. However, using deep learning techniques on drones is not straightforward since computational power and memory constraints exist. In this work, a quantization-based method for learning lightweight convolutional networks is proposed. The ability of the proposed approach to significantly reduce the model size and increase both the feed-forward speed and the accuracy is demonstrated on two different drone-related tasks, i.e., human concept detection and face pose estimation.
A comparative study of representations for folk dances recognition in video

Dance traditions constitute a significant aspect of cultural heritage around the world. The organization, semantic analysis, and retrieval of dance-related multimedia content (i.e., music, video) in databases is, therefore, crucial to their preservation. In this paper we explore the problem of folk dances recognition from video recordings, focusing on Greek folk dances, using different representations for the data. To this end we have employed the well-known Bag of Words model, in combination with dense trajectories, as well as with streaklines descriptors. Furthermore, we have adopted a representation based on Linear Dynamic Systems, including a novel variant that uses dense trajectories descriptors instead of pixel intensities. The performance of the aforementioned representations is evaluated and compared, in a classification scenario involving 13 different dance classes.
Lightweight two-stream convolutional face detection

Video capturing using Unmanned Aerial Vehicles provides cinematographers with impressive shots but requires very adept handling of both the drone and the camera. Deep Learning techniques can be utilized in this process to facilitate the video shooting process by allowing the drone to analyze its input and make intelligent decisions regarding its flight path. Fast and accurate on-board face detection for example can lead the drone towards capturing opportunistic shots, e.g., close ups of persons of importance. However, the constraints imposed by the drones' on-board processing power and memory prohibit the utilization of computationally expensive models. In this paper, we propose a lightweight two-stream fully Convolutional Neural Network for face detection, capable of detecting faces in various settings in real-time using the limited processing power Unmanned Aerial Vehicles possess.
Subclass Error Correcting Output Codes Using Fisher's Linear Discriminant Ratio

Error-Correcting Output Codes (ECOC) with sub-classes reveal a common way to solve multi-class classification problems. According to this approach, a multi-class problem is decomposed into several binary ones based on the maximization of the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast quadratic mutual information (FQMI) procedure. However, FQMI is not applicable on large datasets due to its high algorithmic complexity. In this paper we propose Fisher's Linear Discriminant Ratio (FLDR) as an alternative decomposition criterion which is of much less computational complexity and achieves in most experiments conducted better classification performance. Furthermore, we compare FLDR against FQMI for facial expression recognition over the Cohn-Kanade database.
Variants of dynamic link architecture based on mathematical morphology for frontal face authentication

Two novel variants of dynamic link architecture that are based on mathematical morphology and incorporate coefficients which weigh the contribution of each node in elastic graph matching according to its discriminatory power are developed. They are the so called Morphological Dynamic Link Architecture and the Morphological Signal Decomposition-Dynamic Lint Architecture. The proposed variants are tested for face authentication in a cooperative scenario where the candidates claim an identity to be checked. Their performance is evaluated in terms of their receiver operating characteristic and the equal error rate achieved in M2VTS database. An equal error rate in the range 3.7-6.8% is reported.
Multiplicative Update Rules for Concurrent Nonnegative Matrix Factorization and Maximum Margin Classification

The state-of-the-art classification methods which employ nonnegative matrix factorization (NMF) employ two consecutive independent steps. The first one performs data transformation (dimensionality reduction) and the second one classifies the transformed data using classification methods, such as nearest neighbor/centroid or support vector machines (SVMs). In the following, we focus on using NMF factorization followed by SVM classification. Typically, the parameters of these two steps, e.g., the NMF bases/coefficients and the support vectors, are optimized independently, thus leading to suboptimal classification performance. In this paper, we merge these two steps into one by incorporating maximum margin classification constraints into the standard NMF optimization. The notion behind the proposed framework is to perform NMF, while ensuring that the margin between the projected data of the two classes is maximal. The concurrent NMF factorization and support vector optimization are performed through a set of multiplicative update rules. In the same context, the maximum margin classification constraints are imposed on the NMF problem with additional discriminant constraints and respective multiplicative update rules are extracted. The impact of the maximum margin classification constraints on the NMF factorization problem is addressed in Section VI. Experimental results in several databases indicate that the incorporation of the maximum margin classification constraints into the NMF and discriminant NMF objective functions improves the accuracy of the classification.
Visual voice activity detection based on spatiotemporal information and bag of words

A novel method for Visual Voice Activity Detection (V-VAD) that exploits local shape and motion information appearing at spatiotemporal locations of interest for facial region video description and the Bag of Words (BoW) model for facial region video representation is proposed in this paper. Facial region video classification is subsequently performed based on Single-hidden Layer Feedforward Neural (SLFN) network trained by applying the recently proposed kernel Extreme Learning Machine (kELM) algorithm on training facial videos depicting talking and non-talking persons. Experimental results on two publicly available V-VAD data sets, denote the effectiveness of the proposed method, since better generalization performance in unseen users is achieved, compared to recently proposed state-of-the-art methods.
A review of approximate methods for kernel-based big media data analysis

With the increasing size of today's image and video data sets, standard pattern recognition approaches, like kernel based learning, need to face new challenges. Kernel-based methods require the storage and manipulation of the kernel matrix, having dimensions equal to the number of training samples. When the data set cardinality becomes large, the application of kernel methods becomes intractable. Approximate kernel-based learning approaches have been proposed in order to reduce the time and space complexities of kernel methods, while achieving satisfactory performance. In this paper, we provide a overview of such approximate kernel-based learning approaches finding application in media data analysis.
Copyright protection of 3D images using watermarks of specific spatial structure

A novel blind method for 3D image watermarking, robust against geometric distortions, is proposed. A ternary watermark is embedded in a grayscale or a color 3D volume. Construction of watermarks having appropriate structure enables fast and robust watermark detection even after several geometric distortions of the watermarked volume. Simulation results indicate the ability of the proposed method to deal with the aforementioned attacks. The proposed method is also robust against lossy compression up to a certain compression ratio.
Optimizing subclass discriminant Error Correcting Output Codes using particle swarm optimization

Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem we create larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a discriminant tree creation procedure. This discriminant tree's creation is controlled by a triplet of thresholds that define a set of user defined splitting standards. The selection of the thresholds plays a major role in the classification performance. In our work we show that by optimizing these thresholds via particle swarm optimization we improve significantly the classification performance. Moreover, using Support Vector Machines (SVMs) as classifiers we can optimize in the same time both the thresholds of sub-ECOC and the parameters C and σ of the SVMs, resulting in even better classification performance. Extensive experiments in both real and artificial data illustrate the superiority of the proposed approach in terms of performance.
Stereoscopic video shot classification based on Weighted Linear Discriminant Analysis

In this paper we propose a framework for stereoscopic video shot classification that includes low-level representations exploiting visual and disparity information and determination of optimal discriminant subspaces based on Linear Discriminant Analysis (LDA). Low-level representations are obtained through various color, disparity and texture descriptors which are applied to shot key frames. A new LDA-based subspace representation is proposed aiming at the optimal utilization of both visual and disparity information. The proposed shot classification framework has been evaluated on football stereoscopic videos providing enhanced classification performance and class discrimination, in comparison to using visual information only and standard LDA.