nils-herrmann/clean_scirepeval huggingface dataset:
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection

Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are jointly performed. Specifically, the proposed JELSR joins embedding learning with sparse regression to perform feature selection. To show the effectiveness of the proposed framework, we also provide a method using the weight via local linear approximation and adding the ℓ2,1-norm regularization, and design an effective algorithm to solve the corresponding optimization problem. Furthermore, we also conduct some insightful discussion on the proposed feature selection approach, including the convergence analysis, computational complexity, and parameter determination. In all, the proposed framework not only provides a new perspective to view traditional methods but also evokes some other deep researches for feature selection. Compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression. Promising experimental results on different kinds of data sets, including image, voice data and biological data, have validated the effectiveness of our proposed algorithm.
Facial Skin Beautification Using Adaptive Region-Aware Masks

In this paper, we propose a unified facial beautification framework with respect to skin homogeneity, lighting, and color. A novel region-aware mask is constructed for skin manipulation, which can automatically select the edited regions with great precision. Inspired by the state-of-the-art edit propagation techniques, we present an adaptive edge-preserving energy minimization model with a spatially variant parameter and a high-dimensional guided feature space for mask generation. Using region-aware masks, our method facilitates more flexible and accurate facial skin enhancement while the complex manipulations are simplified considerably. In our beautification framework, a portrait is decomposed into smoothness, lighting, and color layers by an edge-preserving operator. Next, facial landmarks and significant features are extracted as input constraints for mask generation. After three region-aware masks have been obtained, a user can perform facial beautification simply by adjusting the skin parameters. Furthermore, the combinations of parameters can be optimized automatically, depending on the data priors and psychological knowledge. We performed both qualitative and quantitative evaluation for our method using faces with different genders, races, ages, poses, and backgrounds from various databases. The experimental results demonstrate that our technique is superior to previous methods and comparable to commercial systems, for example, PicTreat, Portrait+, and Portraiture.
A biological inspired features based saliency map

A visual attention mechanism is believed to be responsible for the most informative spots in complex scenes. We proposed a novel biologically inspired attention model based on Cortex-like mechanisms and sparse representation. Biological Inspired model, HMAX model, is a feature extraction method and this method is motivated by a quantitative model of visual cortex. This biological inspired feature will be used to build the Saliency Criteria to measure the perspective fields. Saliency Criteria is obtained from Shannon's information entropy and sparse representation. We demonstrate that the proposed model achieves superior accuracy with the comparison to classical approach in static saliency map generation on real data of natural scenes and psychology stimuli patterns.
The pseudo-label scheme in breast tumor classification based on BI-RADS features

The proposed method employs the Breast Imaging Reporting and Data System (BI-RADS) feature to classify the breast tumor. Compared with the ultrasound breast tumor classification methods based on the image, the “semantic gap” between the clinical feature and image feature is eliminated. In order to address the shortage of the labeled data, the pseudo-labeled scheme based on SVM is designed. The SVM classifier is trained by few labeled samples, and the hybrid dataset which contains the pseudo-labeled sample marked by SVM and few labeled samples is adopted to train the decision tree. 500 ultrasound breast tumor cases are collected to evaluate the proposed method. According to the result of the experiment, compared with the decision tree trained by the labeled dataset only, the accuracy of decision tree train by hybrid dataset improves 2.65%, the NPV improves 7.00%, and the Sensitivity increases 3.30%.
Learning Bregman Distance Functions for Structural Learning to Rank

We study content-based learning to rank from the perspective of learning distance functions. Standardly, the two key issues of learning to rank, feature mappings and score functions, are usually modeled separately, and the learning is usually restricted to modeling a linear distance function such as the Mahalanobis distance. However, the modeling of feature mappings and score functions are mutually interacted, and the patterns underlying the data are probably complicated and nonlinear. Thus, as a general nonlinear distance family, the Bregman distance is a suitable distance function for learning to rank, due to its strong generalization ability for distance functions, and its nonlinearity for exploring the general patterns of data distributions. In this paper, we study learning to rank as a structural learning problem, and devise a Bregman distance function to build the ranking model based on structural SVM. To improve the model robustness to outliers, we develop a robust structural learning framework for the ranking model. The proposed model Robust Structural Bregman distance functions Learning to Rank (RSBLR) is a general and unified framework for learning distance functions to rank. The experiments of data ranking on real-world datasets show the superiority of this method to the state-of-the-art literature, as well as its robustness to the noisily labeled outliers.
Spectral-Spatial Constraint Hyperspectral Image Classification

Hyperspectral image classification has attracted extensive research efforts in the recent decade. The main difficulty lies in the few labeled samples versus the high dimensional features. To this end, it is a fundamental step to explore the relationship among different pixels in hyperspectral image classification, toward jointly handing both the lack of label and high dimensionality problems. In the hyperspectral images, the classification task can be benefited from the spatial layout information. In this paper, we propose a hyperspectral image classification method to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated in a hypergraph structure. In the constructed hypergraph, each vertex denotes a pixel in the hyperspectral image. And the hyperedges are constructed from both the distance between pixels in the feature space and the spatial locations of pixels. More specifically, a feature-based hyperedge is generated by using distance among pixels, where each pixel is connected with its K nearest neighbors in the feature space. Second, a spatial-based hyperedge is generated to model the layout among pixels by linking where each pixel is linked with its spatial local neighbors. Both the learning on the combinational hypergraph is conducted by jointly investigating the image feature and the spatial layout of pixels to seek their joint optimal partitions. Experiments on four data sets are performed to evaluate the effectiveness and and efficiency of the proposed method. Comparisons to the state-of-the-art methods demonstrate the superiority of the proposed method in the hyperspectral image classification.
Block Principal Component Analysis With Nongreedy $\ell _{1}$ -Norm Maximization

Block principal component analysis with ℓ1-norm (BPCA-L1) has demonstrated its effectiveness in a lot of visual classification and data mining tasks. However, the greedy strategy for solving the ℓ1-norm maximization problem is prone to being struck in local solutions. In this paper, we propose a BPCA with nongreedy ℓ1-norm maximization, which obtains better solutions than BPCA-L1 with all the projection directions optimized simultaneously. Other than BPCA-L1, the new algorithm has been evaluated against some popular principal component analysis (PCA) algorithms including PCA-L1 and 2-D PCA-L1 on a variety of benchmark data sets. The results demonstrate the effectiveness of the proposed method.
Single Image Super-Resolution With Multiscale Similarity Learning

Example learning-based image super-resolution (SR) is recognized as an effective way to produce a high-resolution (HR) image with the help of an external training set. The effectiveness of learning-based SR methods, however, depends highly upon the consistency between the supporting training set and low-resolution (LR) images to be handled. To reduce the adverse effect brought by incompatible high-frequency details in the training set, we propose a single image SR approach by learning multiscale self-similarities from an LR image itself. The proposed SR approach is based upon an observation that small patches in natural images tend to redundantly repeat themselves many times both within the same scale and across different scales. To synthesize the missing details, we establish the HR-LR patch pairs using the initial LR input and its down-sampled version to capture the similarities across different scales and utilize the neighbor embedding algorithm to estimate the relationship between the LR and HR image pairs. To fully exploit the similarities across various scales inside the input LR image, we accumulate the previous resultant images as training examples for the subsequent reconstruction processes and adopt a gradual magnification scheme to upscale the LR input to the desired size step by step. In addition, to preserve sharper edges and suppress aliasing artifacts, we further apply the nonlocal means method to learn the similarity within the same scale and formulate a nonlocal prior regularization term to well pose SR estimation under a reconstruction-based SR framework. Experimental results demonstrate that the proposed method can produce compelling SR recovery both quantitatively and perceptually in comparison with other state-of-the-art baselines.
Regularized Label Relaxation Linear Regression

Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on  $\ell _{2}$ -norm and  $\ell _{2,1}$ -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.
OpenMRE: A Numerical Platform for MRE Study

Magnetic resonance elastography (MRE) offers a noninvasive solution to visualize the mechanical properties of soft tissue, but the study suffers from expensive magnetic resonance scanning. Moreover, translating MRE wave images into soft tissue elasticity is a nontrivial issue for clinical professionals and healthcare practitioners. An interactive system—OpenMRE—is thus developed with the aid of ImageJ for numerical MRE study. It is comprised of two comparatively independent toolkits, namely MREA for simulation and MREP for interpretation. MREA mainly deals with the forward problem of MRE, and provides a numerical platform to determine the propagation and distribution of specially designed elastic wave. It is possible to numerically study some state-of-the-art paradigms including multisource and multifrequency MRE. The resultant wave images are interpretable in MREP that is designed for the inverse problem of MRE. It consists of the algorithms for phase unwrapping, directional filtering, and elasticity reconstruction. In a word, OpenMRE offers the MRE community a convenient and well-functioning system for interactive MRE study.
Image Quality Assessment Based on Multiscale Geometric Analysis

Reduced-reference (RR) image quality assessment (IQA) has been recognized as an effective and efficient way to predict the visual quality of distorted images. The current standard is the wavelet-domain natural image statistics model (WNISM), which applies the Kullback-Leibler divergence between the marginal distributions of wavelet coefficients of the reference and distorted images to measure the image distortion. However, WNISM fails to consider the statistical correlations of wavelet coefficients in different subbands and the visual response characteristics of the mammalian cortical simple cells. In addition, wavelet transforms are optimal greedy approximations to extract singularity structures, so they fail to explicitly extract the image geometric information, e.g., lines and curves. Finally, wavelet coefficients are dense for smooth image edge contours. In this paper, to target the aforementioned problems in IQA, we develop a novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), and the Weber's law of just noticeable difference (JND). In the proposed framework, MGA is utilized to decompose images and then extract features to mimic the multichannel structure of HVS. Additionally, MGA offers a series of transforms including wavelet, curvelet, bandelet, contourlet, wavelet-based contourlet transform (WBCT), and hybrid wavelets and directional filter banks (HWD), and different transforms capture different types of image geometric information. CSF is applied to weight coefficients obtained by MGA to simulate the appearance of images to observers by taking into account many of the nonlinearities inherent in HVS. JND is finally introduced to produce a noticeable variation in sensory experience. Thorough empirical studies are carried out upon the LIVE database against subjective mean opinion score (MOS) and demonstrate that 1) the proposed framework has good consistency with subjective perception values and the objective assessment results can well reflect the visual quality of images, 2) different transforms in MGA under the new framework perform better than the standard WNISM and some of them even perform better than the standard full-reference IQA model, i.e., the mean structural similarity index, and 3) HWD performs best among all transforms in MGA under the framework.
Surveillance Video Synopsis via Scaling Down Objects

Video synopsis is an effective technique to provide a compact representation of the original video by removing spatiotemporal redundancies and by preserving the essential activities. Most current approaches for video synopsis will cause collisions among objects, especially when the video is condensed much. In this paper, we present an approach for video synopsis to reduce the collisions. Our approach first shifts active objects along the time axis to compact the original video. Then, the sizes of the objects are reduced when collisions occur. Meanwhile, the geometric centroids of the objects will be kept unchanged to preserve the location information. Our contributions are threefold. First, an approach is proposed to decrease collisions in the synopsis video through reducing the sizes of the objects. Second, an optimization framework is developed to indicate the optimal time position and the appropriate reduction coefficient for each object. Finally, some metrics are proposed, and several experiments are carried out to evaluate the proposed approach. The experiments have demonstrated that the synopsis video produced by our approach has much fewer collisions while the compression ratio is high.
A Fine-Grained Image Categorization System by Cellet-Encoded Spatial Pyramid Modeling

In this paper, a new fine-grained image categorization system that improves spatial pyramid matching is developed. In this method, multiple cells are combined into cellets in the proposed categorization model, which are highly responsive to an object's fine categories. The object components are represented by cellets that can connect spatially adjacent cells within the same pyramid level. Here, image categorization can be formulated as the matching between the cellets of corresponding images. Toward an effective matching process, an active learning algorithm that can effectively select a few representative cells for constructing the cellets is adopted. A linear-discriminant-analysis-like scheme is employed to select discriminative cellets. Then, fine-grained image categorization can be conducted with a trained linear support vector machine. Experimental results on three real-world data sets demonstrate that our proposed system outperforms the state of the art.
Patch Alignment for Dimensionality Reduction

Spectral analysis-based dimensionality reduction algorithms are important and have been popularly applied in data mining and computer vision applications. To date many algorithms have been developed, e.g., principal component analysis, locally linear embedding, Laplacian eigenmaps, and local tangent space alignment. All of these algorithms have been designed intuitively and pragmatically, i.e., on the basis of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide a systematic framework for understanding the common properties and intrinsic difference in different algorithms. In this paper, we propose such a framework, named "patch alignment,rdquo which consists of two stages: part optimization and whole alignment. The framework reveals that (1) algorithms are intrinsically different in the patch optimization stage and (2) all algorithms share an almost identical whole alignment stage. As an application of this framework, we develop a new dimensionality reduction algorithm, termed discriminative locality alignment (DLA), by imposing discriminative information in the part optimization stage. DLA can (1) attack the distribution nonlinearity of measurements; (2) preserve the discriminative ability; and (3) avoid the small-sample-size problem. Thorough empirical studies demonstrate the effectiveness of DLA compared with representative dimensionality reduction algorithms.
Learning Multiple Linear Mappings for Efficient Single Image Super-Resolution

Example learning-based superresolution (SR) algorithms show promise for restoring a high-resolution (HR) image from a single low-resolution (LR) input. The most popular approaches, however, are either time- or space-intensive, which limits their practical applications in many resource-limited settings. In this paper, we propose a novel computationally efficient single image SR method that learns multiple linear mappings (MLM) to directly transform LR feature subspaces into HR subspaces. In particular, we first partition the large nonlinear feature space of LR images into a cluster of linear subspaces. Multiple LR subdictionaries are then learned, followed by inferring the corresponding HR subdictionaries based on the assumption that the LR-HR features share the same representation coefficients. We establish MLM from the input LR features to the desired HR outputs in order to achieve fast yet stable SR recovery. Furthermore, in order to suppress displeasing artifacts generated by the MLM-based method, we apply a fast nonlocal means algorithm to construct a simple yet effective similarity-based regularization term for SR enhancement. Experimental results indicate that our approach is both quantitatively and qualitatively superior to other application-oriented SR methods, while maintaining relatively low time and space complexity.
Semi-supervised classification via both label and side information

As for the semi-supervised learning, both label and side information serve as pretty significant indicators for the classification. However, majority of the associated works only focus on one side of the road. In other words, either the label information or the side information is utilized instead of taking both of them into consideration simultaneously. To address the referred defect, we propose a graph-based semi-supervised learning (GSL) problem via building the intrinsic graph and the penalty graph upon both label and side information. To efficiently unravel the proposed GSL problem, a novel quadratic trace ratio (QTR) method is proposed based on solving the associated QTR problem, which is the equivalent counterpart of the GSL problem. Besides, a parameter-free similarity is further derived and utilized. Consequently, a novel semi-supervised classification (SC) algorithm can be summarized by virtue of the proposed GSL problem and QTR method.
Multimodal learning for multi-label image classification

We tackle the challenge of web image classification using additional tags information. Unlike traditional methods that only use the combination of several low-level features, we try to use semantic concepts to represent images and corresponding tags. At first, we extract the latent topic information by probabilistic latent semantic analysis (pLSA) algorithm, and then use multi-label multiple kernel learning to combine visual and textual features to make a better image classification. In our experiments on PASCAL VOC'07 set and MIR Flickr set, we demonstrate the benefit of using multimodal feature to improve image classification. Specifically, we discover that on the issue of image classification, utilizing latent semantic feature to represent images and associated tags can obtain better classification results than other ways that integrating several low-level features.
On Combining Social Media and Spatial Technology for POI Cognition and Image Localization

With fast development of information engineering and social network, people’s locations can be conveniently sensed by spatial technology, such as global positioning systems (GPS), base stations, Wi-Fi access points and even from the appearances of the photos they have taken. The social networks and the online shopping platforms have been gathering billions of users, who share a large amount of images taken in places they live in and visit. We can leverage the social networks to express our opinions about the services and places of interest (POIs). The interactions among users, and user and POIs or services generate big social media data, which have rich information for user, location, and service cognition. Many real-time network applications rely heavily on the accurate social users’ locations. How to sense the locations from multisource social media data is very important and challenging. Thus, in this paper, we give a systematic review of the works that combine social media and spatial technology for POI cognition and image localization.