nils-herrmann/clean_scirepeval huggingface dataset:
Multi-feature ordinal ranking for facial age estimation

In this paper, we propose a multi-feature ordinal ranking (MFOR) method for facial age estimation. Different from most existing facial age estimation approaches where age estimation is treated as a classification or a regression problem, we formulate facial age estimation as a group of ordinal ranking subproblems, and each subproblem derives a separating hyperplane to divide face instances into two groups: samples with age larger than k and samples with labels no larger than k. To better extract complementary information from different facial features, we construct multiple ordinal ranking models, each corresponding to a feature set, and aggregate them into an effective age estimator. Experimental results on two public face aging datasets are presented to demonstrate the efficacy of the proposed method.
Discriminative transfer learning for single-sample face recognition

Discriminant analysis is an important technique for face recognition because it can extract discriminative features to classify different persons. However, most existing discriminant analysis methods fail to work for single-sample face recognition (SSFR) because there is only a single training sample per person such that the within-class variation of this person cannot be estimated in such scenario. In this paper, we present a new discriminative transfer learning (DTL) approach for SSFR, where discriminant analysis is performed on a multiple-sample generic training set and then transferred into the single-sample gallery set. Specifically, our DTL learns a feature projection to minimize the intra-class variation and maximize the inter-class variation of samples in the training set, and minimize the difference between the generic training set and the gallery set, simultaneously. Experimental results on three face datasets including the FERET, CAS-PEAL-R1, and LFW datasets are presented to show the efficacy of our method.
Rate and Power Allocation for Joint Coding and Transmission in Wireless Video Chat Applications

Wireless video chat is a power-consuming and bitrate-intensive application. Unlike video streaming, which is one-way traffic, video chat features distributed two-way traffic relayed via base stations, where resource allocation of a client affects the video quality seen by its communicating partner. In this paper, we study the mechanism design of this application via dynamic pricing, and seek efficiency and fairness of resource utilization. Specifically, we assume that the base station relays video bitstreams and charges a service price on the clients based on the transmission power consumption. Based on the price and a given power budget, the clients allocate bitrate and power for video coding and transmission such that the service price and the distortion seen by their partners are minimized. We study such network dynamics in Stackelberg game-theoretic framework. To solve the problem, we propose a complexity-scalable video encoding method and a power-rate-distortion (PRD) model for video chat. The model is more accurate in describing the PRD characteristics, yet of lower complexity in online updates of its coefficients. Based on the PRD model, we derive the distributed rate and power allocations for the clients. We show that a simple pricing update in the base stations is sufficient for optimal pricing. The proposed algorithms are optimal and converge to the Stackelberg equilibrium. Existing SNR- and power-based pricing schemes could not ensure fairness and efficiency simultaneously. We propose a hybrid pricing scheme that balances these conflicting criteria. Extensive simulations demonstrate superior performance of the proposed methods and solutions.
Perception-Aware Multiple Scalable Video Streaming Over WLANs

In this letter, we consider how to efficiently transmit multiple video programs over IEEE 802.11e WLANs using scalable video coding technique. Scalable video offers flexibilities and functionalities for video adaptation according to the time-varying wireless channel conditions. We examine the perceptible quality impact of scalable video packets and maximize the minimum perceptible video utility of each scalable video stream. The quality of service is optimized by QoS mapping such that scalable video packets with higher impact on perceptible quality are better protected by the enhanced distributed channel access mechanism (EDCA). Using a MAXMIN strategy, we achieve fair distribution of videos to end users based on the spatial, temporal, and quality scalabilities offered by scalable video coding. Our simulation results show the efficacy and performance of the proposed approach.
Extracting good features for motion estimation

Selecting image features whose correspondences can be accurately established between images is a key step in many image processing problems, such as camera and object motion estimation, 3D structure reconstruction, and image registration. In this paper, we present a new method of selecting good features for estimating motion from images. Our approach is different from other existing approaches in that we formulate feature tracking as a signal parameter estimation problem, give a quantitative measure of feature quality in terms of how accurately the feature can be tracked, and can adaptively select features with different shapes and sizes which depend on the local variations of the images. Through the analysis of this feature quality measure, we can characterize the basic properties that allow a feature to be well tracked. Some experimental results are shown to demonstrate the advantages and robustness of the proposed method.
From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection

We propose to summarize a video into a few key objects by selecting representative object proposals generated from video frames. This representative selection problem is formulated as a sparse dictionary selection problem, i.e., choosing a few representatives object proposals to reconstruct the whole proposal pool. Compared with existing sparse dictionary selection based representative selection methods, our new formulation can incorporate object proposal priors and locality prior in the feature space when selecting representatives. Consequently it can better locate key objects and suppress outlier proposals. We convert the optimization problem into a proximal gradient problem and solve it by the fast iterative shrinkage thresholding algorithm (FISTA). Experiments on synthetic data and real benchmark datasets show promising results of our key object summarization approach in video content mining and search. Comparisons with existing representative selection approaches such as K-mediod, sparse dictionary selection and density based selection validate that our formulation can better capture the key video objects despite appearance variations, cluttered backgrounds and camera motions.
Uncorrelated multilinear geometry preserving projections for multimodal biometrics recognition

We propose in this paper a novel supervised manifold learning algorithm, called uncorrelated multilinear geometry preserving projections (UMGPP), incorporating both the Fisher criterion and manifold criterion to learn multiple interrelated subspaces in an iterative manner for efficient multimodal biometric recognition. In contrast to the existing GPP algorithm, UMGPP learns multiple feature subspaces directly in higher order tensor space to preserve the structural information of original biometrics datum and obtains an increased number of uncorrelated projection directions, which enable UMGPP to out-perform GPP for multimodal biometrics recognition. Compared with other conventional information fusion-based multimodal recognition methods, UMGPP well exploits the relationship of different modality of the same individual and learns more efficient subspaces for feature extraction. Experimental results are presented to demonstrate the efficacy of the proposed method.
Facial part displacement effect on template-based gender and ethnicity classification

Visual information such as gender, age and ethnicity play critical roles in human identification. Most of gender and ethnicity recognition research works use the full face considering equal discriminant capability for different face parts. In this paper, we improve the gender and ethnicity recognition, by employing the optimum decision making rule on the confidence level of automatically separated face regions using the modified Golden ratio mask. Faces are preprocessed with multiple base point photometric normalization to prevent facial parts displacement in the noted mask, due to different facial parts' distances of people. SVM is employed as the classifier on the extracted Gabor features of each patch to get its confidence level. The final classification results are obtained based on the output of each patch decision using the optimum decision making rule. Finally, using the most accurate normalization approach for each patch, we could achieve 94% and 98% for gender and ethnicity respectively on a dataset composed of FERET and PEAL frontal face images.
Local Large-Margin Multi-Metric Learning for Face and Kinship Verification

Metric learning has attracted wide attention in face and kinship verification, and a number of such algorithms have been presented over the past few years. However, most existing metric learning methods learn only one Mahalanobis distance metric from a single feature representation for each face image and cannot make use of multiple feature representations directly. In many face-related tasks, we can easily extract multiple features for a face image to extract more complementary information, and it is desirable to learn distance metrics from these multiple features, so that more discriminative information can be exploited than those learned from individual features. To achieve this, we present a large-margin multi-metric learning (LM3L) method for face and kinship verification, which jointly learns multiple global distance metrics under which the correlations of different feature representations of each sample are maximized, and the distance of each positive pair is less than a low threshold and that of each negative pair is greater than a high threshold. To better exploit the local structures of face images, we also propose a local metric learning and local LM3Lmethods to learn a set of local metrics. Experimental results on three face data sets show that the proposed methods achieve very competitive results compared with the state-of-the-art methods.
Minimum convex cost flow problem

Minimum cost flow (MCF) problem is a typical example of network flow problems, for which an additional constraint of cost is added to each flow. Conventional MCF problems consider the cost constraints that are linear functions of flow. In this paper, we extend the MCF problem to cover cost functions that are strictly convex and differentiable, and refer to the problem as convex cost flow problem. To address this problem, we derive the optimality conditions for minimizing convex and differentiable cost functions, and devise an algorithm based on the primal-dual algorithm commonly used in linear programming. The proposed algorithm minimizes the total cost of flow by incrementing the net-workflow along augmenting paths of minimum cost. Simulation results are provided to demonstrate the efficacy of the proposed algorithm.
Complete discriminative feature learning: A new approach for heterogeneous face recognition

In this paper, we propose a new feature learning approach called complete discriminative feature learning (CDFL) for heterogeneous face recognition. Unlike most existing heterogeneous face recognition methods where hand-crafted feature descriptors are used for face representation, the proposed CD-FL aims to learn an optimal weighted discriminative image filter to improve learning discriminative filters, so that complete discriminative information is exploited and the feature difference between different modalities is effectively reduced, simultaneously. Experimental results shows that our approach consistently outperforms the state-of-the-art methods.
Nonlinear metric learning for visual tracking

We propose a nonlinear metric learning (NML) method for visual tracking. Instead of utilizing the hand-crafted similarity measures, the NML tracker can automatically learn distance metrics from training data itself to categorize object and backgrounds in visual tracking. To exploit the nonlinear structures of samples, the NML tracker seeks several hierarchical nonlinear transformations by adopting the neural network architectures to map candidates and template into a latent subspace where the distance of each positive pair is smaller than that of each negative pair. In this learned metric space, the candidate that maintains the minimum distance to the template is treated as the final tracking result. Evaluation on 20 challenging videos shows the efficacy of the NML tracker.
Color appearance-based approach to robust tracking and recognition of multiple people

In this paper, we propose an automated tracking system which can robustly recognize multiple people in the video sequence. By checking the foreground coverage measure, the system first segments and localizes isolated people from multiple foreground regions. We then model each tracked person by his/her color histogram so that the system is capable of performing optimal recognition after occlusion from a statistical viewpoint. Since shadow could severely degrade color histograms, we propose a novel shadow-removal scheme to suppress shadow effects and thus help improve the reliability of people recognition. The people recognition technique is also extended to identify people that re-enter the scene of a closed-environment.
A framework for measuring video similarity and its application to video query by example

The usefulness of a video database relies on whether the video of interest can be easily located. To allow exploring, browsing, and retrieving videos according to their visual content, efficient techniques for evaluating the visual similarity between different video clips are necessary. We present a framework for measuring video similarity across different resolutions-both spatial and temporal. In particular, the video clips to be compared can be properly aligned through the use of suitable weighting functions and alignment constraints. Dynamic programming techniques are employed to obtain the video similarity measure with a reasonable computational cost. An application to searching MPEG compressed video by example is presented to demonstrate the potential use of the proposed video similarity measure.
Learning Cascaded Deep Auto-Encoder Networks for Face Alignment

In this paper, we propose a new cascaded deep auto-encoder networks (CDAN) approach for face alignment. Our framework consists of a global exemplar-based deep auto-encoder network (GEDAN) and a series of localized deep auto-encoder networks (LDAN) in a cascaded fashion. The global network takes a low-resolution holistic facial image as input and generates a preliminary facial landmark configuration. The following localized networks sample pose-indexed local features around current landmark positions, and refine the landmark positions with increasingly higher image resolutions. Our network architectures are designed to achieve greater robustness against pose variations as well as higher landmark estimation accuracy. Experimental results on three datasets show that the proposed approach achieves superior alignment accuracy with real-time speed.
A camera-based system for early detection of drowning incidents

We present in this paper a camera-based system for detecting drowning incidents in a swimming pool at the earliest possible stage. The system consists of two main parts: a vision component which can reliably detect and track swimmers in spite of large scene variations of monitored pool areas, and an event-inference module which parses observation sequences of swimmer features for possible drowning behavioral signs. The vision component employs a model-based approach to represent and differentiate background pool areas and foreground swimmers. The event-inference module is constructed based on a finite state machine, which integrates several reasoning rules formulated from universal motion characteristics of drowning swimmers. Possible drowning incidents are quickly detected using a sequential change detection algorithm. The proposed system has been applied to a number of video clips of simulated drowning, and promising results have been obtained.
An unsupervised approach to dominant video scene clustering

In this paper, we propose an unsupervised approach for dominant scene clustering in sports video. By adopting a customized peer group filtering (PGF) to identify prototypes for k-means clustering, dominant scenes can be clustered based on shot color histogram (SCH). Meanwhile, the number of clusters can automatically be determined by estimating the time coverage of dominant scenes. To improve the computational efficiency and clustering accuracy, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to project SCH into reduced dimensional spaces. The prototypes obtained by PGF can also be served as sufficient and representative training data for LDA. Such good training data ensures LDA outperforms PCA with better clustering performance in more reduced feature dimension.
Fall Incidents Detection for Intelligent Video Surveillance

We present in this paper an intelligent video surveillance system to detect human fall incidents for enhanced safety in indoor environments. The system consists of two main parts: a vision component which can reliably detect and track moving people in the view of a camera, and an event-inference module which parses observation sequences of people features for possible falling behavioral signs. In particular, we extract the aspect ratio of a person as observation feature, based on which fall incidents are detected as abrupt changes in the feature space. Our experiments show that the proposed approach can robustly detect human falls in real time
Joint power allocation and bit loading for enhanced SVC video downlink transmissions over SDMA/OFDMA networks

We address in this paper the utility maximization of scalable video transmission under limited radio resource in a multi-user, multi-antenna downlink. The proposed utility maximization framework comprises two parts: a utility analysis of scalable video based on approximated rate-distortion function and a resource allocation scheme by joint power allocation and bit loading. Scalable video packets are entitled with differentiated priority due to the inherent dependency relationship in a layer-based encoding scheme. First, we analyze the rate-utility of scalable video by packet prioritization, which plays a key role in video utility maximization under a fixed rate constraint. Then to cater for diverse rate-utility character and time-varying network condition for multiple scalable video downlink over SDMA/OFDMA system, a joint power allocation and bit loading scheme is proposed to increase the overall video utility under limited radio resource. The resource allocation is accomplished by three important procedures, user grouping, bit loading and power adaptation. The performance of the proposed scheme is validated by comparing with conventional radio resource allocation schemes.
Modified Kolmogorov-Smirnov metric for shot boundary detection

A new metric extended from the existing Kolmogorov–Smirnov measure is proposed for detecting shot boundaries of video. Exploiting more fine-grained differences between the cumulative histograms of two successive frames, the proposed metric improves both the recall and precision of shot boundary detection. Experiments conducted on a comprehensive video set show that the new metric outperforms other widely-used histogram-based metrics.
Joint packet prioritization and QoS mapping for SVC over wlans

The emerging H.264/AVC extension, SVC encoding standard facilitates the truncation of bitstreams at certain points to fit in with wireless network variations. In this paper, we propose a joint packet prioritization and QoS mapping strategy based on priority analysis of SVC packets and the exploration of the service differentiations among IEEE 802.11e EDCA access categories. The proposed scheme enables the interaction among different layers, providing differentiated services for scalable video packets. The cross-layer optimization is performed based on SVC packet information at application layer, differentiated access categories at MAC layer and interface queue (IFQ) control at link layer. Based on these cross-layer information, the proposed joint packet prioritization and QoS mapping strategy minimizes the packet loss impact of visual quality. The proposed approach shows significant performance improvement when compared to conventional schemes for SVC streaming over 802.11e wireless networks.
People counting using iterative mean-shift fitting with symmetry measure

This paper presents a new method for counting the number of persons from video images. Conventional people counting methods can be classified into the learning-tracking based techniques. They either require elaborated human model learned using AdaBoost or sophisticated tracking algorithms by particle filtering. The proposed algorithm performs people counting by segmenting group of people in a cluttered scene into individuals. By assuming human body is bilaterally symmetric, the proposed method first determines a probability map of symmetry using a local energy function. Taking the highest symmetry pixel as the initial position, our method then employs the mean-shift technique to fit each person in the foreground. The procedure of mean-shift fitting is repeated until all the foreground regions are exhausted. The advantages of the proposed method lie in its simplicity and capability of counting multiple human targets without stringent learning and tracking constraints. Experimental results show that the proposed method is robust and efficient in handling real-life video data of different scenarios.
Perceptually optimized error resilient transcoding using attention-based intra refresh

While deployment of wireless channels has become widespread and fast-growing for mobile applications, transmitting data over these existing error-prone networks can be very unreliable and challenging due to time-varying interference and channel errors. Many error-resilient algorithms have been proposed to provide adequate resilient features in order to protect video data from channel errors. However, these algorithms often aim to achieve the optimal decoded video quality in terms of mean square error without any consideration for the visual quality. In this paper, we present a perceptually error-resilient method for video transcoding based on the attention-based intra refresh technique and the characteristics of the human visual system to enhance the perceptual performance of the transcoded video. Specifically, the foveated just noticeable distortion and visual attention models are employed to estimate the perceptual loss impact due to error propagation for allocating intra-refreshed macroblocks in the transcoded video. Experimental results show that the proposed method can achieve a much better performance than the existing methods in terms of both the visual quality and perceptual quality measure.
Reconstructing Videos From Multiple Compressed Copies

A single source video may be compressed using different encoders with different settings. In the context of online video sharing, many such video copies exist, and an end user may have access to a few of them and would like to reconstruct a video sequence with quality superior to all available copies. In this paper, we propose a scheme to improve the video quality by projecting the reconstructed video onto the quantization constraint sets defined by multiple video copies. Experimental results show that the proposed method is capable of improving video quality both subjectively and objectively.