nils-herrmann/clean_scirepeval huggingface dataset:
Baseline Results for Violence Detection in Still Images

Recognizing objectionable content draws more and more attention nowadays given the rapid proliferation of images and videos on the Internet. Although there are some investigations about violence video detection and pornographic information filtering, very few existing methods touch on the problem of violence detection in still images. However, given its potential use in violence webpage filtering, online public opinion monitoring and some other aspects, recognizing violence in still images is worth being deeply investigated. To this end, we first establish a new database containing 500 violence images and 1500 non-violence images. And we use the Bag-of-Words (BoW) model which is frequently adopted in image classification domain to discriminate violence images and non-violence images. The effectiveness of four different feature representations are tested within the BoW framework. Finally the baseline results for violence image detection on our newly built database are reported.
Practical camera auto-calibration based on object appearance and motion for traffic scene visual surveillance

Camera calibration, as a fundamental issue in computer vision, is indispensable in many visual surveillance applications. Firstly, calibrated camera can help to deal with perspective distortion of object appearance on image plane. Secondly, calibrated camera makes it possible to recover metrics from images which are robust to scene or view angle changes. In addition, with calibrated cameras, we can make use of prior information of 3D models to estimate 3D pose of objects and make object detection or tracking more robust to noise and occlusions. In this paper, we propose an automatic method to recover camera models from traffic scene surveillance videos. With only the camera height H measured, we can completely recover both intrinsic and extrinsic parameters of cameras based on appearance and motion of objects in videos. Experiments are conducted in different scenes and experimental results demonstrate the effectiveness and practicability of our approach, which can be adopted in many traffic scene surveillance applications.
Contextual Operation for Recommender Systems

With the rapid growth of various applications on the Internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information is a significant factor in modeling the user behavior, various context-aware recommendation methods have been proposed recently. The state-of-the-art context modeling methods usually treat contexts as certain dimensions similar to those of users and items, and capture relevances between contexts and users/items. However, such kind of relevance has much difficulty in explanation. Some works on multi-domain relation prediction can also be used for the context-aware recommendation, but they have limitations in generating recommendations under a large amount of contextual information. Motivated by recent works in natural language processing, we represent each context value with a latent vector, and model the contextual information as a semantic operation on the user and item. Besides, we use the contextual operating tensor to capture the common semantic effects of contexts. Experimental results show that the proposed Context Operating Tensor (COT) model yields significant improvements over the competitive compared methods on three typical datasets. From the experimental results of COT, we also obtain some interesting observations which follow our intuition.
Feature coding via vector difference for image classification

An effective image representation is important to an image classification task. The most popular image representation framework utilizes a feature coding algorithm to encode the extracted low-level feature descriptors into a vector representation. In this paper, we analyze the recently developed feature coding methods in a general way. According to their common characteristics, we propose a new coding scheme to perform feature coding based on the vector difference in a high-dimensional space which is obtained by explicit feature maps. As we illustrate, our method has promising results with small codebook sizes and generalizes most existing coding methods in a unified form.
Learning efficient codes for 3D face recognition

Face representation based on the visual codebook becomes popular because of its excellent recognition performance, in which the critical problem is how to learn the most efficient codes to represent the facial characteristics. In this paper, we introduce the quadtree clustering algorithm to learn the facial-codes to boost 3D face recognition performance. The merits of quadtree clustering come from: (1) It is robust to data noises; (2) It can adaptively assign clustering centers according to the density of data distribution. We make a comparison between quadtree and some widely used clustering methods, such as g-means, k-means, normalized-cut and mean-shift. Experimental results show that using the facial- codes learned by quadtree clustering gives the best performance for 3D face recognition.
A general algorithm for document skew angle estimation

We propose a new method for calculating the skew angle of scanned document images. The method is designed to be insensitive to document layout, line spacing, font, graphics/images and, most importantly, the language or script of the document. This is achieved by examining the Fourier spectra of blocks of the document image for peak pairs corresponding to the angle of skew. From a histogram compiled over all blocks in the document image the correct skew angle can be determined to within approximately 0.5/spl deg/, regardless of document script, even when the image contains considerable graphical information.
Iris Localization via Pulling and Pushing

Iris localization is a critical module in iris recognition because it defines the inner and outer boundaries of iris region used for feature analysis. State-of-the-art iris localization methods need to implement a brute-force search of the large parameter space, which is time-consuming and sensitive to noises. This paper proposes a novel iris localization method based on a spring force-driven iteration scheme. First, the coarse localization of pupil is obtained by an AdaBoost-based iris detection method. Then the radial edge points are detected in polar coordinate, which contribute a force on the circle center based on Hooke's law. Finally the center and radius of pupil and iris are refined according to the composition of forces from all points. After 3-4 iterations, the precise location of iris could be found. Experimental results show that our method is faster and more accurate than state-of-the-art iris localization
Efficient iris recognition by characterizing key local variations

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2 255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.
Learning Appearance Primitives of Iris Images for Ethnic Classification

Iris pattern is commonly regarded as a kind of phenotypic feature without relation to genes. In our previous work, we argued that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In this paper, we propose a novel ethnic classification method based on learning appearance primitives of iris images. So we not only confirm that iris texture is race related, but also try to find out which kinds of iris visual primitives make iris images look different between Asian and non-Asian. In our scheme, we learned a small finite vocabulary of micro-structures, which are called iris-textons, to represent visual primitives of iris images. Then we use iris-texton histogram to capture the difference between iris textures. Finally iris images are grouped into two race categories, Asian and non-Asian, by support vector machine (SVM). Based on the proposed method, we get a higher correct classification rate (CCR) of 91.02% than our previous method on a database containing 2400 iris samples.
Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation

We propose a foreground segmentation method based on convolutional networks. To predict the label of a pixel in an image, the model takes a hierarchical context as the input, which is obtained by combining multiple context patches on different scales. Short range contexts depict the local details, while long range contexts capture the object-scene relationships in an image. Early means that we combine the context patches of a pixel into a hierarchical one before any trainable layers are learned, i.e., early-combing. In contrast, late-combing means that the combination occurs later, e.g., when the convolutional feature extractor in a network has already been learned. We find that it is vital for the whole model to jointly learn the patterns of contexts on different scales in our task. Experiments show that early-combing performs better than late-combing. On the dataset1 built up by Baidu IDL2 for a latest person segmentation contest, our method beats all the competitors with a considerable margin. Qualitative results also show that the proposed method is almost ready for practical application.
Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices

The quality of iris images on mobile devices is significantly degraded due to hardware limitations and less constrained environments. Traditional iris recognition methods cannot achieve high identification rate using these low-quality images. To enhance the performance of mobile identification, we develop a deep feature fusion network that exploits the complementary information presented in iris and periocular regions. The proposed method first applies maxout units into the convolutional neural networks (CNNs) to generate a compact representation for each modality and then fuses the discriminative features of two modalities through a weighted concatenation. The parameters of convolutional filters and fusion weights are simultaneously learned to optimize the joint representation of iris and periocular biometrics. To promote the iris recognition research on mobile devices under near-infrared (NIR) illumination, we publicly release the CASIA-Iris-Mobile-V1.0 database, which in total includes 11 000 NIR iris images of both eyes from 630 Asians. It is the largest NIR mobile iris database as far as we know. On the newly built CASIA-Iris-M1-S3 data set, the proposed method achieves 0.60% equal error rate and 2.32% false non-match rate at false match rate  $=10^{-5}$ , which are obviously better than unimodal biometrics as well as traditional fusion methods. Moreover, the proposed model requires much fewer storage spaces and computational resources than general CNNs.
Conditional High-Order Boltzmann Machines for Supervised Relation Learning

Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.
A Code-Level Approach to Heterogeneous Iris Recognition

Matching heterogeneous iris images in less constrained applications of iris biometrics is becoming a challenging task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities or filtered features. In contrast, this paper proposes a code-level approach in heterogeneous iris recognition. The non-linear relationship between binary feature codes of heterogeneous iris images is modeled by an adapted Markov network. This model transforms the number of iris templates in the probe into a homogenous iris template corresponding to the gallery sample. In addition, a weight map on the reliability of binary codes in the iris template can be derived from the model. The learnt iris template and weight map are jointly used in building a robust iris matcher against the variations of imaging sensors, capturing distance, and subject conditions. Extensive experimental results of matching cross-sensor, high-resolution versus low-resolution and, clear versus blurred iris images demonstrate the code-level approach can achieve the highest accuracy in compared with the existing pixel-level, feature-level, and score-level solutions.
Extraction of noise robust rotation invariant texture features via multichannel filtering

An efficient and accurate method of extracting rotation invariant texture features via a multichannel Gabor filtering technique is presented. A main focus of the paper is a thorough investigation into the optimum parameter settings for the method. Experiments include the exploration of different frequency combinations, sampling intervals and number of features. The optimum settings are used to test the method's texture classification abilities on a database of over 1320 images originating from 44 different texture classes. The resistance to noise is measured via the addition of various levels of Gaussian noise to each image before classification.
A General Nonlinear Embedding Framework Based on Deep Neural Network

Recently there has been increasing interest in deep neural network due to its powerful represent ability in several successful applications such as speech recognition and image classification. In this paper, we propose a general nonlinear embedding framework based on deep neural network which can be utilized to implement a family of dimensionality reduction algorithms. The objective function of our framework consists of two terms: 1) an embedding term which transforms the input to a low-dimensional representation with a multilayer network, and 2) a regularization term which computes the reconstruction error of the original input by unrolling the multilayer network to a deep auto encoder. We adopt a layer-by-layer pretraining procedure to obtain good initial weights for the network, and then minimize the objective function by back propagating derivatives of the two terms. To evaluate the proposed framework, we perform face recognition and digit classification experiments. The experiments demonstrate that the proposed framework achieves better results than the state-of-the-art algorithms. The success of our framework further verifies deep neural network's advantages in representation learning.
Practical Camera Calibration From Moving Objects for Traffic Scene Surveillance

We address the problem of camera calibration for traffic scene surveillance, which supplies a connection between 2-D image features and 3-D measurement. It is helpful to deal with appearance distortion related to view angles, establish multiview correspondences, and make use of 3-D object models as prior information to enhance surveillance performance. A convenient and practical camera calibration method is proposed in this paper. With the camera height H measured as the only user input, we can recover both intrinsic and extrinsic parameters of the camera based on redundant information supplied by moving objects in monocular videos. All cases of traffic scene layouts are considered and corresponding solutions are given to make our method applicable to almost all kinds of traffic scenes in reality. Numerous experiments are conducted in different scenes, and experimental results demonstrate the accuracy and practicability of our approach. It is shown that our approach can be effectively adopted in all kinds of traffic scene surveillance applications.
Global and local training for moving object classification in surveillance-oriented scene

This paper presents a new training framework for multi-class moving object classification in surveillance-oriented scene. In many practical multi-class classification tasks, the instances are close to each other in the input feature space when they have similar features. These instances may have different class labels. Since the moving objects may have various view and shape, the above phenomenon is common in multi-class moving object classification. In our framework, firstly the input feature space is divided into several local clusters. Then, global training and local training are carried out sequential with an efficient online learning based algorithm. The induced global classifier is used to assign candidate instances to the most reliable clusters. Meanwhile, the trained local classifiers within those clusters can determine which classes the candidate instances belong to. Our experimental results illustrate the effectiveness of our method for moving object classification in surveillance-oriented scene.
Optimized 3D Lighting Environment Estimation for Image Forgery Detection

Image forgery is becoming a growing threat to information credibility. Among all kinds of image forgeries, photographic composites of human faces have very serious impacts. To combat this kind of forgery, some forensic methods propose to estimate the 3D lighting environments from different faces and investigate the consistency between them. Although they are very effective, existing 3D lighting-based forensic methods are limited by many simplifying assumptions about the surface reflection model, among which convexity and constant reflectance are two critical ones. In this paper, we propose an optimized 3D lighting estimation method by incorporating a more general surface reflection model. In this model, we relax the convexity and constant reflectance assumptions by taking the occlusion geometry and surface texture information into consideration. The proposed reflection model is more general and accurate; hence, it can achieve better lighting estimation accuracy and more reliable discrimination performance. Comprehensive experiments on both synthetic and real data sets validate the correctness and efficacy of the proposed method. Comparisons with two existing 3D lighting-based forensic methods also demonstrate the superiority of the proposed method for detecting face splicing.
Robust Recovery of Corrupted Low-RankMatrix by Implicit Regularizers

Low-rank matrix recovery algorithms aim to recover a corrupted low-rank matrix with sparse errors. However, corrupted errors may not be sparse in real-world problems and the relationship between ℓ1 regularizer on noise and robust M-estimators is still unknown. This paper proposes a general robust framework for low-rank matrix recovery via implicit regularizers of robust M-estimators, which are derived from convex conjugacy and can be used to model arbitrarily corrupted errors. Based on the additive form of half-quadratic optimization, proximity operators of implicit regularizers are developed such that both low-rank structure and corrupted errors can be alternately recovered. In particular, the dual relationship between the absolute function in ℓ1 regularizer and Huber M-estimator is studied, which establishes a connection between robust low-rank matrix recovery methods and M-estimators based robust principal component analysis methods. Extensive experiments on synthetic and real-world data sets corroborate our claims and verify the robustness of the proposed framework.
Biologically Inspired Features for Scene Classification in Video Surveillance

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective , and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.
Deformable DAISY Matcher for robust iris recognition

Iris is rich of texture information for reliable personal identification. However, nonlinear deformation of iris pattern caused by pupil dilation or contraction raises a grand challenge to iris recognition. This paper proposes a novel iris recognition method namely Deformable DAISY Matcher (DDM) for robust iris feature matching. Firstly, dense DAISY descriptors are extracted to represent regional iris features, which are robust against intra-class variations of iris images. Then a set of iris key points are localized on the feature map. Finally deformation tolerant matching strategy is proposed to match corresponding key points of iris images. Experimental results on two iris image databases demonstrate DDM is better than state-of-the-art iris recognition methods.
Mixture clustering using multidimensional histograms for skin detection

Mixture models are frequently used to fit skin color distributions in various color spaces. However, the high computational cost of the conventional EM algorithm makes it intractable for large data sets. We propose a novel algorithm for estimating the parameters of mixture models. Multidimensional histograms are incorporated into the EM framework to group neighboring datapoints and reduce the size of the data set. We adopt this method to build Gaussian mixture models of skin color and compare the performance of models with different number of components. Further experiments on synthetic data show the efficiency of our method as a general approach to data clustering.