nils-herrmann/clean_scirepeval huggingface dataset:
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering

For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an initial graph with only a few nearest but most reliable pairwise relations, new reliable relations are discovered by an assumption of reliability preservation, i.e., the reliable relations will preserve their reliabilities in the learnt projection subspace. We formulate the idea as a cross entropy (CE) minimization problem to reduce the discrepancy between two Bernoulli distributions parameterized by the updated distances and the existing relation graph respectively. Furthermore, to overcome the imbalanced distribution of samples, a Boosting-like strategy is proposed to balance the discovered relations over all clusters. To evaluate the proposed method, extensive experiments are performed with various trajectory clustering tasks, including motion segmentation, time series clustering and crowd detection. The results demonstrate that ReDSFA can discover reliable intra-cluster relations with high precision, and competitive clustering performance can be achieved in comparison with state-of-the-art.
Gesture recognition using temporal template based trajectories

In this paper, a novel approach of hand gesture recognition is proposed. The spatial-temporal trajectory of hand gesture is first tracked by the mean shift embedded particle filter (MSEPF), and then represented in a static image using temporal template. Hand gestures are recognized by a two-layer classifier, which is based on statistical shape and orientation analysis of such temporal template based trajectories (TTBT). Experimental results show that our algorithm has high recognition rate.
Texture removal for adaptive level set based iris segmentation

Level set based active contour method has been proposed for iris segmentation in recent years, but it can not converge to iris contours in real applications because of its sensitivity to local gradient extremes due to the complex iris texture. In this paper, a novel scheme is proposed to remove local gradient extremes before using level set directly. Firstly, we use two orthogonal ordinal filters to obtain robust gradient map. Then we localize the iris region on the gradient map by an improved Hough transform. After that, a Semantic Iris Contour Map is generated by combining the spatial information of coarse iris location and the gradient map as the edge indicator for level set segmentation. For robust and accurate segmentation, we propose a convergence criterion and a means of updating the parameters for level set. Finally, the accurate segmentation is obtained by the robust adaptive level set method. Encouraging results on ICE 2005 database and CASIA v3 database show the efficiency and effectiveness of our method.
Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution

Most modern face super-resolution methods resort to convolutional neural networks (CNN) to infer highresolution (HR) face images. When dealing with very low resolution (LR) images, the performance of these CNN based methods greatly degrades. Meanwhile, these methods tend to produce over-smoothed outputs and miss some textural details. To address these challenges, this paper presents a wavelet-based CNN approach that can ultra-resolve a very low resolution face image of 16 × 16 or smaller pixelsize to its larger version of multiple scaling factors (2×, 4×, 8× and even 16×) in a unified framework. Different from conventional CNN methods directly inferring HR images, our approach firstly learns to predict the LR’s corresponding series of HR’s wavelet coefficients before reconstructing HR images from them. To capture both global topology information and local texture details of human faces, we present a flexible and extensible convolutional neural network with three types of loss: wavelet prediction loss, texture loss and full-image loss. Extensive experiments demonstrate that the proposed approach achieves more appealing results both quantitatively and qualitatively than state-ofthe- art super-resolution methods.
Efficient image gradient based vehicle localization

This paper reports novel algorithms for the efficient localization and recognition of traffic in traffic scenes. The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correlations based directly on image gradient data, which can be computed "on the fly." The algorithms are therefore very well suited to real-time implementation. The algorithms make use of two a priori sources of knowledge about the scene and the objects expected: (1) the ground-plane constraint and (2) the fact that the overall shape of road vehicles is strongly rectilinear. Additional efficiency is derived from making the weak perspective assumption. These assumptions are valid in the road traffic application domain. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles in several traffic scenes demonstrates the efficiency and robustness of context-based image understanding in road traffic scene analysis. The limitations of the algorithms are also addressed in the paper.
LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution

The low spatial resolution of light-field image poses significant difficulties in exploiting its advantage. To mitigate the dependency of accurate depth or disparity information as priors for light-field image super-resolution, we propose an implicitly multi-scale fusion scheme to accumulate contextual information from multiple scales for super-resolution reconstruction. The implicitly multi-scale fusion scheme is then incorporated into bidirectional recurrent convolutional neural network, which aims to iteratively model spatial relations between horizontally or vertically adjacent sub-aperture images of light-field data. Within the network, the recurrent convolutions are modified to be more effective and flexible in modeling the spatial correlations between neighboring views. A horizontal sub-network and a vertical sub-network of the same network structure are ensembled for final outputs via stacked generalization. Experimental results on synthetic and real-world data sets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in peak signal-to-noise ratio and gray-scale structural similarity indexes, which also achieves superior quality for human visual systems. Furthermore, the proposed method can enhance the performance of light field applications such as depth estimation.
Robust visual tracking based on simplified biologically inspired features

We address the problem of robust appearance-based visual tracking. First, a set of simplified biologically inspired features (SBIF) is proposed for object representation and the Bhattacharyya coefficient is used to measure the similarity between the target model and candidate targets. Then, the proposed appearance model is combined into a Bayesian state inference tracking framework utilizing the SIR (sampling importance resampling) particle filter to propagate sample distributions over time. Numerous experiments are conducted and experimental results demonstrate that our algorithm is robust to partial occlusions and variations of illumination and pose, resistent to nearby distractors, as well as possesses the state-of-the-art tracking accuracy.
Tracking Blurred Object with Data-Driven Tracker

Motion blur is very common in the low quality of image sequences and videos captured by low speed of cameras. Object tracking without accounting for the motion blur would easily fail in these kinds of videos. We propose a new data-driven tracker in the particle filter framework to address this problem without deblurring the image sequences. The motion blur is detected by exploring the property of the blurred input image through Fourier analysis. The appearance model is integrated with a set of motion blur kernels which could reflect different blur effects in real scenes. The motion model is improved to be more robust to sudden motion of the target object. To evaluate the proposed algorithm, several challenging videos with significant motion blur are used in the experiments. The experimental results demonstrate the robustness and accuracy of our algorithm.
Rapid and robust human detection and tracking based on omega-shape features

This paper proposes a novel method for rapid and robust human detection and tracking based on the omega-shape features of people's head-shoulder parts. There are two modules in this method. In the first module, a Viola-Jones type classifier and a local HOG (Histograms of Oriented Gradients) feature based AdaBoost classifier are combined to detect head-shoulders rapidly and effectively. Then, in the second module, each detected head-shoulder is tracked by a particle filter tracker using local HOG features to model target's appearance, which shows great robustness in scenarios of crowding, background distractors and partial occlusions. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.
Face contour construction with multiple information

In this paper, we present a new method to obtain the contours of the colored faces. First, we introduce a new nonlinear skin classifier to detect the human skin information in images. Then, a split machine (SM) places an adaptive grid over an image according to the low-level information detected in the representative regions. By filtering the redundant points extracted from the actively divided grid, we obtain a set of points that can coarsely construct the contour of the detailed skin region. Finally, we refine the contour by computing the local minimum energy of each point. Experimental results show that our proposed approach is capable of constructing the contours of the colored faces.
Learning Boosted Asymmetric Classifiers for Object Detection

Object detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR) , and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods.
Semantic-based traffic video retrieval using activity pattern analysis

A semantic based retrieval framework for traffic video sequences is proposed. In order to estimate the low-level motion data, a cluster tracking algorithm is developed. A novel hierarchical self-organizing map is applied to learn the activity patterns. By using activity pattern analysis and semantic concepts assignment, a set of activity models is generated, which is used as the indexing key for accessing video clips and individual vehicles in the semantic level. The proposed retrieval framework supports various queries including query by keywords, query by sketch and multiple object queries.
Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting

Localizing objects of interest in images when provided with only image-level labels is a challenging visual recognition task. Previous efforts have required carefully designed features and have difficulty in handling images with cluttered backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications. In this paper, we propose an efficient and effective learning framework called MILinear, which is able to learn an object localization model from large-scale data without using bounding box annotations. We integrate rich general prior knowledge into a learning model using a large pre-trained convolutional network. Moreover, to reduce ambiguity in positive images, we present a bag-splitting algorithm that iteratively generates new negative bags from positive ones. We evaluate the proposed approach on the challenging Pascal VOC 2007 dataset, and our method outperforms other state-of-the-art methods by a large margin; some results are even comparable to fully supervised models trained with bounding box annotations. To further demonstrate scalability, we also present detection results on the ILSVRC 2013 detection dataset, and our method outperforms supervised deformable part-based model without using box annotations.
Adaptive skin detection using multiple cues

This paper presents an adaptive approach to skin detection. First, we propose a nonlinear relationship among R, G and B components and use a closed curve to identify the skin cluster region. Then, a split machine is designed that aids the extraction of the pixels with similar low-level features from images. Finally, a nonlinear skin color classifier with an adaptive threshold is developed by analyzing the properties of the extracted pixels in the HSL, YCbCr, YUV and YIQ color spaces. Experimental results show that our proposed method works very well in skin detection.
Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval

Cross-modal retrieval has recently drawn much attention due to the widespread existence of multimodal data. It takes one type of data as the query to retrieve relevant data objects of another type, and generally involves two basic problems: the measure of relevance and coupled feature selection. Most previous methods just focus on solving the first problem. In this paper, we aim to deal with both problems in a novel joint learning framework. To address the first problem, we learn projection matrices to map multimodal data into a common subspace, in which the similarity between different modalities of data can be measured. In the learning procedure, the ℓ2-norm penalties are imposed on the projection matrices separately to solve the second problem, which selects relevant and discriminative features from different feature spaces simultaneously. A multimodal graph regularization term is further imposed on the projected data,which preserves the inter-modality and intra-modality similarity relationships.An iterative algorithm is presented to solve the proposed joint learning problem, along with its convergence analysis. Experimental results on cross-modal retrieval tasks demonstrate that the proposed method outperforms the state-of-the-art subspace approaches.
An iris image synthesis method based on PCA and super-resolution

It is very important for the performance evaluation of iris recognition algorithms to construct very large iris databases. However, limited by the real conditions, there are no very large common iris databases now. In this paper, an iris image synthesis method based on principal component analysis (PCA) and super-resolution is proposed. The iris recognition algorithm based on PCA is first introduced and then, iris image synthesis method is presented. The synthesis method first constructs coarse iris images with the given coefficients. Then, synthesized iris images are enhanced using super-resolution. Through controlling the coefficients, we can create many iris images with specified classes. Extensive experiments show that the synthesized iris images have satisfactory cluster and the synthesized iris databases can be very large.
Noise removal and impainting model for iris image

Noise removal is an important problem for iris recognition. If the iris regions were not correctly segmented in iris images, segmented iris regions possibly include noises, namely eyelashes, eyelids, reflections and pupil. Noises influence the features of both noise regions and their neighboring regions, which will result in poor recognition performance. To solve this problem, this paper proposes a method for removing noises and impainting iris images. The whole procedure includes three steps: 1) localization and normalization, 2) noise removal based on phase congruency and 3) iris image impainting. A series of experiments show that the proposed method has encouraging performance for improving the recognition accuracy.
Online Text-independent Writer Identification Based on Temporal Sequence and Shape Codes

In this paper we present a novel method for online text-independent writer identification. Most of the existing writer identification techniques require the data to be from a specific text which is not applicable to cases where such text is not available, such as in criminal justice systems when text documents with different content need to be compared. Text-independent approaches often require a large amount of data to be confident of good results. We propose temporal sequence and shape codes to encode online handwriting. Temporal sequence codes (TSC) are to characterize trajectory in speed and pressure change in writing, and shape codes (SC) are to characterize direction of trajectory in writing handwriting. For TSC, we use two different codes to encode speed and pressure to codebook: stroke temporal sequence codes (STSC) and neighbor temporal sequence codes (NTSC). At identification stage, we implement decision and fusion strategy to identify writer. Experimental results show that our proposed method can improve the identification accuracy with a small number of characters. Moreover, we find that the proposed method is even effective for cross-language (English & Chinese) writer identification.
Effects of watermarking on iris recognition performance

Protection of biometric data and templates is a crucial issue for the security of biometric systems, and biometric watermarking is introduced for this purpose. However, watermarking introduces extra information into the biometric data (biometric images or biometric feature templates) which leads to certain distortion. In addition, watermarked images are always subject to the risk of being attacked. Hence, whether and how biometric recognition performance will be affected by biometric watermarking deserves investigation. In this paper, we make a first attempt in such investigations by studying two application scenarios in the context of iris recognition, namely protection of iris templates by hiding them in cover images as watermarks (iris watermarks), and protection of iris images by watermarking them. Experimental results suggest that watermark embedding in iris images does not introduce detectable decreases on iris recognition performance whereas recognition performance drops significantly if iris watermarks suffer from severe attacks.
Real-world gender recognition using multi-order LBP and localized multi-boost learning

This paper presents a new approach for real-world gender recognition, where images are captured under uncontrolled environments with various poses, illuminations and expressions. While a large number of gender recognition methods have been introduced in recent years, most of them describe each image in a single feature space or simple combination of multiple individual spaces, which can not be powerful enough to alleviate the noise in real-world scenarios. To address this, we propose exploring multiple order local binary patterns (MOLBP) as features for learning, and develop a localized multi-boost learning (LMBL) algorithm to combine the different features for classification. Experimental results show that the proposed algorithm outperforms state-of-the-art methods in two real-world datasets.
Robust nose detection in 3D facial data using local characteristics

The problem of detecting the feature points arises in many fields of science and engineering. In this paper, we focus on the 3D face range data and propose a robust scheme to solve a specific problem, i.e. locating the nose lip and nose ridge using the local statistic features and included angle curve. This work is very significant to 3D face modelling, recognition and registration. The key features of our method are the fully automated processing, the ability to deal with noisy and incomplete input data, the immunity to the rotation and translation and the adaptability to the different resolution. The experimental results in different databases fully show the robust and feasibility of the proposed method.
A new attempt to gait-based human identification

The authors propose a simple but efficient approach to gait recognition. For each image sequence, an improved background subtraction procedure is first used to accurately extract spatial silhouettes of a walker from the background. Then, an eigenspace transformation to time-varying silhouette shapes is performed to realize feature extraction. The nearest neighbor classifier using spatio-temporal correlation or the normalized Euclidean distance measure is finally utilized in the lower-dimensional eigenspace for recognition, and some additional personalized physical properties are selected for the validation of final decision Experimental results on a small database show that the proposed algorithm has an encouraging recognition rate with relatively lower computational cost.
Script and language identification from document images

In this paper we present a detailed review of current script and language identification techniques. The main criticism of the existing techniques is that most of them rely on either connected component analysis or character segmentation. We go on to present a new method based on texture analysis for script identification which does not require character segmentation. A uniform text block on which texture analysis can be performed is produced from a document image via simple processing. Multiple channel (Gabor) filters and grey level co-occurrence matrices are used in independent experiments in order to extract texture features. Classification of test documents is made based on the features of training documents using the K-NN classifier. Initial results of over 95% accuracy on the classification of 105 rest decrements from 7 scripts are very promising. The method shows robustness with respect to noise, the presence of foreign characters or numerals, and can be applied to very small amounts of text.