nils-herrmann/clean_scirepeval huggingface dataset:
Representative Vector Machines: A Unified Framework for Classical Classifiers

Classifier design is a fundamental problem in pattern recognition. A variety of pattern classification methods such as the nearest neighbor (NN) classifier, support vector machine (SVM), and sparse representation-based classification (SRC) have been proposed in the literature. These typical and widely used classifiers were originally developed from different theory or application motivations and they are conventionally treated as independent and specific solutions for pattern classification. This paper proposes a novel pattern classification framework, namely, representative vector machines (or RVMs for short). The basic idea of RVMs is to assign the class label of a test example according to its nearest representative vector. The contributions of RVMs are twofold. On one hand, the proposed RVMs establish a unified framework of classical classifiers because NN, SVM, and SRC can be interpreted as the special cases of RVMs with different definitions of representative vectors. Thus, the underlying relationship among a number of classical classifiers is revealed for better understanding of pattern classification. On the other hand, novel and advanced classifiers are inspired in the framework of RVMs. For example, a robust pattern classification method called discriminant vector machine (DVM) is motivated from RVMs. Given a test example, DVM first finds its ${k}$ -NNs and then performs classification based on the robust M-estimator and manifold regularization. Extensive experimental evaluations on a variety of visual recognition tasks such as face recognition (Yale and face recognition grand challenge databases), object categorization (Caltech-101 dataset), and action recognition (Action Similarity LAbeliNg) demonstrate the advantages of DVM over other classifiers.
Multispectral palm image fusion for accurate contact-free palmprint recognition

In this paper, we propose to improve the verification performance of a contract-free palmprint recognition system by means of feature- level image registration and pixel-level fusion of multi-spectral palm images. Our method involves image acquisition via a dedicated device under contact-free and multi-spectral environment, preprocessing to locate region of interest (ROI) from each individual hand images, feature-level registration to align ROIs from different spectral images in one sequence and fusion to combine images from multiple spectra. The advantages of the proposed method include better hygiene and higher verification performance. Given a database composed of images from 330 hands, two out of four state of the art fusion strategies offer significant performance gain and the best equal error rate (EER) is 0.5%.
Steganalysis of data hiding in binary images

It is commonly believed that data hiding in binary image can't be detected due to little and noise like changes of data hiding. This work proposes a steganalytic technique that can detect the existence of hidden message embedded in binary images. The technique is based on statistical analysis of the image statistics such as black-and-white border complexity, block noisiness and block coarseness. The probability of both false positive and false negative rates can also be estimated. The experimental results show that the proposed method can detect hiding fact very effectively.
Transferring deep representation for NIR-VIS heterogeneous face recognition

One task of heterogeneous face recognition is to match a near infrared (NIR) face image to a visible light (VIS) image. In practice, there are often a few pairwise NIR-VIS face images but it is easy to collect lots of VIS face images. Therefore, how to use these unpaired VIS images to improve the NIR-VIS recognition accuracy is an ongoing issue. This paper presents a deep TransfeR NIR-VIS heterogeneous facE recognition neTwork (TRIVET) for NIR-VIS face recognition. First, to utilize large numbers of unpaired VIS face images, we employ the deep convolutional neural network (CNN) with ordinal measures to learn discriminative models. The ordinal activation function (Max-Feature-Map) is used to select discriminative features and make the models robust and lighten. Second, we transfer these models to NIR-VIS domain by fine-tuning with two types of NIR-VIS triplet loss. The triplet loss not only reduces intra-class NIR-VIS variations but also augments the number of positive training sample pairs. It makes fine-tuning deep models on a small dataset possible. The proposed method achieves state-of-the-art recognition performance on the most challenging CASIA NIR-VIS 2.0 Face Database. It achieves a new record on rank-1 accuracy of 95.74% and verification rate of 91.03% at FAR=0.001. It cuts the error rate in comparison with the best accuracy [27] by 69%.
Partial Least Squares based subwindow search for pedestrian detection

In this paper, we propose a Partial Least Squares based sub-window search method for pedestrian detection, by which the detection speed can be improved effectively while maintaining high detection accuracy. Firstly, a sparse search is implemented to find all the possible locations containing parts of a pedestrian. Then a pre-learned Partial Least Squares regression model is applied to estimate the displacements of the subwindows to guide them towards the approximate locations of the pedestrians. Finally, we conduct a dense search around the approximate locations to obtain the exact locations of the pedestrians. Experiments on the INRIA dataset demonstrate that our method greatly reduces the number of search windows, which leads to much fewer feature extraction in the detection phase. Thus, it is about 10 times faster than the sliding window method with a jump step of 8 × 8.
A simple and robust super resolution method for light field images

Light field cameras generate low-resolution images due to the tradeoff between spatial and angular resolution. Traditional light field super-resolution (LFSR) methods depend on prior knowledge of depth information. This paper presents a projection-based LFSR solution without prior information based on redefinition of the mapping function between disparity and shearing shift. Moreover, simplified variational regularization is imposed in global optimization formulation to the rendered high-resolution images. Both a synthetic dataset and a real-world dataset of light field images captured by a self-developed light field camera are used to demonstrate the state-of-the-art performance of the proposed method.
EDA Approach for Model Based Localization and Recognition of Vehicles

We address the problem of model based recognition. Our aim is to localize and recognize road vehicles from monocular images in calibrated scenes. A deformable 3D geometric vehicle model with 12 parameters is set up as prior information and Bayesian Classification Error is adopted for evaluation of fitness between the model and images. Using a novel evolutionary computing method called EDA (Estimation of Distribution Algorithm), we can not only determine the 3D pose of the vehicle, but also obtain a 12 dimensional vector which corresponds to the 12 shape parameters of the model. By clustering obtained vectors in the parameter space, we can recognize different types of vehicles. Experimental results demonstrate the effectiveness of the approach to vehicles of different types and poses. Thanks to EDA, we can not only localize and recognize vehicles, but also show the whole evolution procedure of the deformable model which gradually fits the image better and better.
A hierarchical self-organizing approach for learning the patterns of motion trajectories

The understanding and description of object behaviors is a hot topic in computer vision. Trajectory analysis is one of the basic problems in behavior understanding, and the learning of trajectory patterns that can be used to detect anomalies and predict object trajectories is an interesting and important problem in trajectory analysis. In this paper, we present a hierarchical self-organizing neural network model and its application to the learning of trajectory distribution patterns for event recognition. The distribution patterns of trajectories are learnt using a hierarchical self-organizing neural network. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Compared with the existing neural network structures that are used to learn patterns of trajectories, our network structure has smaller scale and faster learning speed, and is thus more effective. Experimental results using two different sets of data demonstrate the accuracy and speed of our hierarchical self-organizing neural network in learning the distribution patterns of object trajectories.
Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles

We address the problem of model-based object recognition. Our aim is to localize and recognize road vehicles from monocular images or videos in calibrated traffic scenes. A 3-D deformable vehicle model with 12 shape parameters is set up as prior information, and its pose is determined by three parameters, which are its position on the ground plane and its orientation about the vertical axis under ground-plane constraints. An efficient local gradient-based method is proposed to evaluate the fitness between the projection of the vehicle model and image data, which is combined into a novel evolutionary computing framework to estimate the 12 shape parameters and three pose parameters by iterative evolution. The recovery of pose parameters achieves vehicle localization, whereas the shape parameters are used for vehicle recognition. Numerous experiments are conducted in this paper to demonstrate the performance of our approach. It is shown that the local gradient-based method can evaluate accurately and efficiently the fitness between the projection of the vehicle model and the image data. The evolutionary computing framework is effective for vehicles of different types and poses is robust to all kinds of occlusion.
Enhanced biologically inspired model

It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challenging databases. However, BIM has the following three problems: a very heavy computational cost due to dense input, a disputable pooling operation in modeling relations of the visual cortex, and blind feature selection in a feed-forward framework. To solve these problems, we develop an enhanced BIM (EBIM), which removes uninformative input by imposing sparsity constraints, utilizes a novel local weighted pooling operation with stronger physiological motivations, and applies a feedback procedure that selects effective features for combination. Empirical studies on the CalTech5 database and CalTech101 database show that EBIM is more effective and efficient than BIM. We also apply EBIM to the MIT-CBCL street scene database to show it achieves comparable performance in comparison with the current best performance. Moreover, the new system can process images with resolution 128 times 128 at a rate of 50 frames per second and enhances the speed 20 times at least in comparison with BIM in common applications.
Recognizing Night Walkers Based on One Pseudoshape Representation of Gait

Gait is a promising biometric cue which can facilitate the recognition of human beings, particularly when other biometrics are unavailable. Existing work for gait recognition, however, lays more emphasis on the problem of daytime walker recognition and overlooks the significance of walker recognition at night. This paper deals with the problem of recognizing nighttime walkers. We take advantage of infrared gait patterns to accomplish this task: 1) Walker detection is improved using intensity compensation-based background subtraction; 2) pseudoshape-based features are proposed to describe gait patterns; 3) the dimension of gait features is reduced through the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques; 4) temporal cues are exploited in the form of the relevant component analysis (RCA) learning; 5) the nearest neighbor classifier is used to recognize unknown gait. Experimental results justify the effectiveness of our method and show that our method has an encouraging potential for the application in surveillance systems.
Complementary Cohort Strategy for Multimodal Face Pair Matching

Face pair matching is the task of determining whether two face images represent the same person. Due to the limited expressive information embedded in the two face images as well as various sources of facial variations, it becomes a quite difficult problem. Toward the issue of few available images provided to represent each face, we propose to exploit an extra cohort set (identities in the cohort set are different from those being compared) by a series of cohort list comparisons. Useful cohort coefficients are then extracted from both sorted cohort identities and sorted cohort images for complementary information. To augment its robustness to complicated facial variations, we further employ multiple face modalities owing to their complementary value to each other for the face pair matching task. The final decision is made by fusing the extracted cohort coefficients with the direct matching score for all the available face modalities. To investigate the capacity of each individual modality on matching faces, the cohort behavior, and the performance achieved using our complementary cohort strategy, we conduct a set of experiments on two recently collected multimodal face databases. It is shown that using different modalities leads to different face pair matching performance. For each modality, employing our cohort scheme significantly reduces the equal error rate. By applying the proposed multimodal complementary cohort strategy, we achieve the best performance on our face pair matching task.
Improved 3D lighting environment estimation for image forgery detection

3D lighting environment is an important clue in an image that can be used for image forgery detection. Existing forensic methods exploring lighting environment consistency are based on many assumptions, among which convexity and constant reflectance of the surface are two critical ones. In this paper, we propose an improved 3D lighting environment estimation method based on a more general surface reflection model. We relax the two assumptions by incorporating the local geometry and texture information into our position dependent reflection model. The proposed model is more realistic for objects like human faces which are non-convex and textured. Experiments show that the proposed method can achieve improved lighting environment estimation accuracy compared to the previous method and has better forgery detection efficacy.
Video steganalysis based on the constraints of motion vectors

In this paper, we focus on detecting data hiding in motion vectors of compressed video and propose a new steganalytic algorithm based on the mutual constraints of motion vectors. The constraints of motion vectors from multiple frames are analyzed and formulized by three functions, then statistical features are extracted based on these functions. Moreover, we also incorporate calibration method to improve the detection accuracy. Experimental results demonstrate that the proposed method can effectively attack typical motion-vector-based video steganography.
Real time hand tracking by combining particle filtering and mean shift

Particle filter and mean shift are two successful approaches taken in the pursuit of robust tracking. Both of them have their respective strengths and weaknesses. In this paper, we proposed a new tracking algorithm, the mean shift embedded particle filter (MSEPF), to integrate advantages of the two methods. Compared with the conventional particle filter, the MSEPF leads to more efficient sampling by shifting samples to their neighboring modes, overcoming the degeneracy problem, and requires fewer particles to maintain multiple hypotheses, resulting in low computational cost. When applied to hand tracking, the MSEPF tracks hand in real time, saving much time for later gesture recognition, and it is robust to the hand's rapid movement and various kinds of distractors.
The first ICB* competition on iris recognition

Iris recognition becomes an important technology in our society. Visual patterns of human iris provide rich texture information for personal identification. However, it is greatly challenging to match intra-class iris images with large variations in unconstrained environments because of noises, illumination variation, heterogeneity and so on. To track current state-of-the-art algorithms in iris recognition, we organized the first ICB* Competition on Iris Recognition in 2013 (or ICIR2013 shortly). In this competition, 8 participants from 6 countries submitted 13 algorithms totally. All the algorithms were trained on a public database (e.g. CASIA-Iris-Thousand ) and evaluated on an unpublished database. The testing results in terms of False Non-match Rate (FNMR) when False Match Rate (FMR) is 0.0001 are taken to rank the submitted algorithms.
Multi-modal Subspace Learning with Joint Graph Regularization for Cross-Modal Retrieval

This paper investigates the problem of cross-modal retrieval, where users can search results across various modalities by submitting any modality of query. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. To address this problem, we propose a joint graph regularized multi-modal subspace learning (JGRMSL) algorithm, which integrates inter-modality similarities and intra-modality similarities into a joint graph regularization to better explore the cross-modal correlation and the local manifold structure in each modality of data. To obtain good class separation, the idea of Linear Discriminant Analysis (LDA) is incorporated into the proposed method by maximizing the between-class covariance of all projected data and minimizing the within-class covariance of all projected data. Experimental results on two public cross-modal datasets demonstrate the effectiveness of our algorithm.
Adaptive multi-view clustering via cross trace lasso

We propose a novel multi-view clustering method by learning auto-regression problems under structural constraints and treating the regression coefficients as new feature representations for the cluster partition. In particular, we take the data intrinsic correlation structure into account. Correlated data under one view tend to be also related under another view and are likely to fall into the same group. Therefore we pair the data matrix from one view and the regression coefficient from a different view together to meet a trace Lasso constraint, which adaptively adjusts the sparsity of regression coefficients in order to promote consistent data correlations across views. Then a joint low-rank constraint is further imposed to encourage similar regression coefficients for the same samples under distinct views. Finally, we develop an effective algorithm to optimize the objective function. And experimental results demonstrate that our method is useful and fairly competitive compared with other state-of-the-art multi-view clustering methods.
Group Activity Recognition Based on ARMA Shape Sequence Modeling

In this paper, we propose a system identification approach for group activity recognition in traffic surveillance. Statistical shape theory is used to extract features, and then ARMA (autoregressive and moving average) is adopted for feature learning and activity identification. Here only a few points, instead of the complete trajectory of each object are used to describe the dynamic information of group activity. And ARMA is employed to learn activity sequences. The performance of the proposed method is proved by experiments on 570 video sequences, with the average recognition rate of 88% (compared with 81% of HMM). The extracted features are invariant to zoom, pan and tilt, which is also proved in the experiments.
Ordinal Measures for Iris Recognition

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.
Trajectory Series Analysis based Event Rule Induction for Visual Surveillance

In this paper, a generic rule induction framework based on trajectory series analysis is proposed to learn the event rules. First the trajectories acquired by a tracking system are mapped into a set of primitive events that represent some basic motion patterns of moving object. Then a minimum description length (MDL) principle based grammar induction algorithm is adopted to infer the meaningful rules from the primitive event series. Compared with previous grammar rule based work on event recognition where the rules are all defined manually, our work aims to learn the event rules automatically. Experiments in a traffic crossroad have demonstrated the effectiveness of our methods. Shown in the experimental results, most of the grammar rules obtained by our algorithm are consistent with the actual traffic events in the crossroad. Furthermore the traffic lights rule in the crossroad can also be leaned correctly with the help of eliminating the irrelevant trajectories.
Deformable Object Matching via Deformation Decomposition Based 2D Label MRF

Deformable object matching, which is also called elastic matching or deformation matching, is an important and challenging problem in computer vision. Although numerous deformation models have been proposed in different matching tasks, not many of them investigate the intrinsic physics underlying deformation. Due to the lack of physical analysis, these models cannot describe the structure changes of deformable objects very well. Motivated by this, we analyze the deformation physically and propose a novel deformation decomposition model to represent various deformations. Based on the physical model, we formulate the matching problem as a two-mensional label Markov Random Field. The MRF energy function is derived from the deformation decomposition model. Furthermore, we propose a two-stage method to optimize the MRF energy function. To provide a quantitative benchmark, we build a deformation matching database with an evaluation criterion. Experimental results show that our method outperforms previous approaches especially on complex deformations.