nils-herrmann/clean_scirepeval huggingface dataset:
Video Synopsis in Complex Situations

Video synopsis is an effective technique for surveillance video browsing and storage. However, most of the existing video synopsis approaches are not suitable for complex situations, especially crowded scenes. This is because these approaches heavily depend on the preprocessing results of foreground segmentation and multiple objects tracking, but the preprocessing techniques usually achieve poor performance in crowded scenes. To address this problem, we propose a comprehensive video synopsis approach which can be applied to scenes with drastically varying crowdedness. The proposed approach differs significantly from the existing methods, and has several appealing properties. First, we propose to detect the crowdedness of a given video, then, extract object tubes in sparse periods and extract video clips in crowded periods, respectively. Through such a solution, the poor performance of preprocessing techniques in crowded scenes can be avoided by extracting the whole video frames. Second, we propose a group-partition algorithm which can discovers the relationships among moving objects and alleviates several segmentation and tracking errors. Third, a group-based greedy optimization algorithm is proposed to automatically determine the length of a synopsis video. Besides, we present extensive experiments that demonstrate the effectiveness and efficiency of the proposed approach.
Semisupervised Learning With Parameter-Free Similarity of Label and Side Information

As for semisupervised learning, both label information and side information serve as pivotal indicators for the classification. Nonetheless, most of related research works utilize either label information or side information instead of exploiting both of them simultaneously. To address the referred defect, we propose a graph-based semisupervised learning (GSL) problem according to both given label information and side information. To solve the GSL problem efficiently, two novel self-weighted strategies are proposed based on solving associated equivalent counterparts of a GSL problem, which can be widely applied to a spectrum of biobjective optimizations. Different from a conventional technique to amalgamate must-link and cannot-link into a single similarity for convenient optimization, we derive a new parameter-free similarity, upon which intrinsic graph and penalty graph can be separately developed. Consequently, a novel semisupervised classification algorithm can be summarized correspondingly with a theoretical analysis.
Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework

To benefit the skin care, this paper aims to design an automatic and effective visual analysis framework, with the expectation of recognizing the skin disease from a given image conveying the disease affected surface. This task is nontrivial, since it is hard to collect sufficient well-labeled samples. To address such problem, we present a novel transfer learning model, which is able to incorporate external knowledge obtained from the rich and relevant Web images contributed by grassroots. In particular, we first construct a target domain by crawling a small set of images from vertical and professional dermatological websites. We then construct a source domain by collecting a large set of skin disease related images from commercial search engines. To reinforce the learning performance in the target domain, we initially build a learning model in the target domain, and then seamlessly leverage the training samples in the source domain to enhance this learning model. The distribution gap between these two domains are bridged by a linear combination of Gaussian kernels. Instead of training models with low-level features, we resort to deep models to learn the succinct, invariant, and high-level image representations. Different from previous efforts that focus on a few types of skin diseases with a small and confidential set of images generated from hospitals, this paper targets at thousands of commonly seen skin diseases with publicly accessible Web images. Hence the proposed model is easily repeatable by other researchers and extendable to other disease types. Extensive experiments on a real-world dataset have demonstrated the superiority of our proposed method over the state-of-the-art competitors.
Saliency Detection by Multiple-Instance Learning

Saliency detection has been a hot topic in recent years. Its popularity is mainly because of its theoretical meaning for explaining human attention and applicable aims in segmentation, recognition, etc. Nevertheless, traditional algorithms are mostly based on unsupervised techniques, which have limited learning ability. The obtained saliency map is also inconsistent with many properties of human behavior. In order to overcome the challenges of inability and inconsistency, this paper presents a framework based on multiple-instance learning. Low-, mid-, and high-level features are incorporated in the detection procedure, and the learning ability enables it robust to noise. Experiments on a data set containing 1000 images demonstrate the effectiveness of the proposed framework. Its applicability is shown in the context of a seam carving application.
Zernike-Moment-Based Image Super Resolution

Multiframe super-resolution (SR) reconstruction aims to produce a high-resolution (HR) image using a set of low-resolution (LR) images. In the process of reconstruction, fuzzy registration usually plays a critical role. It mainly focuses on the correlation between pixels of the candidate and the reference images to reconstruct each pixel by averaging all its neighboring pixels. Therefore, the fuzzy-registration-based SR performs well and has been widely applied in practice. However, if some objects appear or disappear among LR images or different angle rotations exist among them, the correlation between corresponding pixels becomes weak. Thus, it will be difficult to use LR images effectively in the process of SR reconstruction. Moreover, if the LR images are noised, the reconstruction quality will be affected seriously. To address or at least reduce these problems, this paper presents a novel SR method based on the Zernike moment, to make the most of possible details in each LR image for high-quality SR reconstruction. Experimental results show that the proposed method outperforms existing methods in terms of robustness and visual effects.
Naked people retrieval based on Adaboost learning

Presents a learning scheme for judging whether there are any naked people in an image. First, learning vector quantization is used to build several classifiers based on the low-level features, such as color histogram, region shape, texture and etc., which are extracted from the images. The best classifier performs a recognition ratio of 81.3%, so the Adaboost learning method is applied to combine these classifiers to form a stronger one, and the final classification achieves a result of 86.0% on the test set. Adaboost's ability in multi-feature analysis is emphasized, and the proposed algorithm is important both for the blue-picture-filter in the WWW and for semantic image indexing in content-based image retrieval. In experiments, the groundtruth is made of 1,200 images and the test set is independent from the training set.
Approximate Low-Rank Projection Learning for Feature Extraction

Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many applications. As an excellent unsupervised feature extraction method, latent low-rank representation (LatLRR) has shown its power in extracting salient features. However, LatLRR has the following three disadvantages: 1) the dimension of features obtained using LatLRR cannot be reduced, which is not preferred in feature extraction; 2) two low-rank matrices are separately learned so that the overall optimality may not be guaranteed; and 3) LatLRR is an unsupervised method, which by far has not been extended to the supervised scenario. To this end, in this paper, we first propose to use two different matrices to approximate the low-rank projection in LatLRR so that the dimension of obtained features can be reduced, which is more flexible than original LatLRR. Then, we treat the two low-rank matrices in LatLRR as a whole in the process of learning. In this way, they can be boosted mutually so that the obtained projection can extract more discriminative features. Finally, we extend LatLRR to the supervised scenario by integrating feature extraction with the ridge regression. Thus, the process of feature extraction is closely related to the classification so that the extracted features are discriminative. Extensive experiments are conducted on different databases for unsupervised and supervised feature extraction, and very encouraging results are achieved in comparison with many state-of-the-arts methods.
Long-Short-Term Features for Dynamic Scene Classification

Dynamic scene classification has been extensively studied in computer vision due to its widespread applications. The key to dynamic scene classification lies in jointly characterizing spatial appearance and temporal dynamics to achieve informative representation, which remains an outstanding task in the literature. In this paper, we propose a unified framework to extract spatial and temporal features for dynamic scene representation. More specifically, we deploy two variants of deep convolutional neural networks to encode spatial appearance and short-term dynamics into short-term deep features (STDF). Based on STDF, we propose using the autoregressive moving average model to extract long-term frequency features (LTFF). By combining STDF and LTFF, we establish the long–short-term feature (LSTF) representations of dynamic scenes. The LSTF characterizes both spatial and temporal patterns of dynamic scenes for comprehensive and information representation that enables more accurate classification. Extensive experiments on three-dynamic scene classification benchmarks have shown that the proposed LSTF achieves high performance and substantially surpasses the state-of-the-art methods.
Visual-Context Boosting for Eye Detection

Eye detection plays an important role in many practical applications. This paper presents a novel two-step scheme for eye detection. The first step models an eye by a newly defined visual-context pattern (VCP), and the second step applies semisupervised boosting for precise detection. VCP describes both the space and appearance relations between an eye region (region of eye) and a reference region (region of reference). The context feature of a VCP is extracted by using the integral image. Aiming to reduce the human labeling efforts, we apply semisupervised boosting, which integrates the context feature and the Haar-like features for precise eye detection. Experimental results on several standard face data sets demonstrate that the proposed approach is effective, robust, and efficient. We finally show that this approach is ready for practical applications.
Semantic Video Shot Segmentation Based on Color Ratio Feature and SVM

With the fast development of video semantic analysis, there has been increasing attention to the typical issue of the semantic analysis of soccer program. Based on the color feature analysis, this paper focuses on the video shot segmentation problem from the perspective of semantic analysis, i.e. the semantic shot segmentation. Most existing works segment and classify the shot by using the dominant color of the field in soccer video. In this paper, we extend the traditional dominant color to several semantic colors, and define the color ratio feature. Then, the support vector machine is used for shot classification. The experimental result shows that the color ratio feature is useful to improve the performance. Furthermore, considering the temporal variations in the semantic color due to environment, an adaptive semantic color extraction algorithm is proposed, and the influence of the number of semantic colors on classification precision is evaluated also.
Efficient Video Stitching Based on Fast Structure Deformation

In computer vision, video stitching is a very challenging problem. In this paper, we proposed an efficient and effective wide-view video stitching method based on fast structure deformation that is capable of simultaneously achieving quality stitching and computational efficiency. For a group of synchronized frames, firstly, an effective double-seam selection scheme is designed to search two distinct but structurally corresponding seams in the two original images. The seam location of the previous frame is further considered to preserve the interframe consistency. Secondly, along the double seams, 1-D feature detection and matching is performed to capture the structural relationship between the two adjacent views. Thirdly, after feature matching, we propose an efficient algorithm to linearly propagate the deformation vectors to eliminate structure misalignment. At last, image intensity misalignment is corrected by rapid gradient fusion based on the successive over relaxation iteration (SORI) solver. A principled solution to the initialization of the SORI significantly reduced the number of iterations required. We have compared favorably our method with seven state-of-the-art image and video stitching algorithms as well as traditional ones. Experimental results show that our method outperforms the existing ones compared in terms of overall stitching quality and computational efficiency.
Classifying Discriminative Features for Blur Detection

Blur detection in a single image is challenging especially when the blur is spatially-varying. Developing discriminative blur features is an open problem. In this paper, we propose a new kernel-specific feature vector consisting of the information of a blur kernel and the information of an image patch. Specifically, the kernel specific-feature is composed of the multiplication of the variance of filtered kernel and the variance of filtered patch gradients. The feature origins from a blur-classification theorem and its discrimination can also be intuitively explained. To make the kernel-specific features useful for real applications, we build a pool of kernels consisting of motion-blur kernels, defocus-blur (out-of-focus) kernels, and their combinations. By extracting such features followed by the classifiers, the proposed algorithm outperforms the state-of-the-art blur detection method. Experimental results on public databases demonstrate the effectiveness of the proposed method.
How to represent scenes for classification?

Object-based scene image representations can effectively capture the semantic meanings of a scene. However, they usually neglect a scene's structure information. In this paper, we propose a novel and effective detector-based scene representation method for scene classification. In particular, we extract object features by object detectors. By sensible principal component analysis, we obtain a compact representation vector of objects in a scene image. To capture the scene layout, we then train lots of deformable part models to form a scene response vector. By concatenating these two vectors we use a linear support vector machine for scene classification. When combining with DeCAF  in a special way, our method is even more powerful on complex scene categorization. Experimental results on the MIT indoor database show that our approach achieves state-of-the-art performance on scene classification compared with several popular methods.
Rank Preserving Discriminant Analysis for Human Behavior Recognition on Wireless Sensor Networks

With the rapid development of the intelligent sensing and the prompt growing industrial safety demands, human behavior recognition has received a great deal of attentions in industrial informatics. To deploy an utmost scalable, flexible, and robust human behavior recognition system, we need both innovative sensing electronics and suitable intelligence algorithms. Wireless sensor networks (WSNs) open a novel way for human behavior recognition, because the heavy computation can be immediately transferred to a network server. In this paper, a new scheme for human behavior recognition on WSNs is proposed, which transmits activities' signals compressed by Hamming compressed sensing to the network server and conducts behavior recognition through a collaboration between a new dimension reduction algorithm termed rank preserving discriminant analysis (RPDA) and a nearest neighbor classifier. RPDA encodes local rank information of within-class samples and discriminative information of the between-class under the framework of Patch Alignment Framework. Experiments are conducted on the SCUT Naturalistic 3D Acceleration-based Activity (SCUT NAA) dataset and demonstrate the effectiveness of RPDA for human behavior recognition.
From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms

Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising.
Multivariate Multilinear Regression

Conventional regression methods, such as multivariate linear regression (MLR) and its extension principal component regression (PCR), deal well with the situations that the data are of the form of low-dimensional vector. When the dimension grows higher, it leads to the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. However, little attention has been paid to such a problem. This paper first adopts an in-depth investigation to the USP in PCR, which answers three questions: 1) Why is USP produced? 2) What is the condition for USP, and 3) How is the influence of USP on regression. With the help of the above analysis, the principal components selection problem of PCR is presented. Subsequently, to address the problem of PCR, a multivariate multilinear regression (MMR) model is proposed which gives a substitutive solution to MLR, under the condition of multilinear objects. The basic idea of MMR is to transfer the multilinear structure of objects into the regression coefficients as a constraint. As a result, the regression problem is reduced to find two low-dimensional coefficients so that the principal components selection problem is avoided. Moreover, the sample size needed for solving MMR is greatly reduced so that USP is alleviated. As there is no closed-form solution for MMR, an alternative projection procedure is designed to obtain the regression matrices. For the sake of completeness, the analysis of computational cost and the proof of convergence are studied subsequently. Furthermore, MMR is applied to model the fitting procedure in the active appearance model (AAM). Experiments are conducted on both the carefully designed synthesizing data set and AAM fitting databases verified the theoretical analysis.
Human Carrying Status in Visual Surveillance

A person’s gait changes when he or she is carrying an object such as a bag, suitcase or rucksack. As a result, human identification and tracking are made more difficult because the averaged gait image is too simple to represent the carrying status. Therefore, in this paper we first introduce a set of Gabor based human gait appearance models, because Gabor functions are similar to the receptive field profiles in the mammalian cortical simple cells. The very high dimensionality of the feature space makes training difficult. In order to solve this problem we propose a general tensor discriminant analysis (GTDA), which seamlessly incorporates the object (Gabor based human gait appearance model) structure information as a natural constraint. GTDA differs from the previous tensor based discriminant analysis methods in that the training converges. Existing methods fail to converge in the training stage. This makes them unsuitable for practical tasks. Experiments are carried out on the USF baseline data set to recognize a human’s ID from the gait silhouette. The proposed Gabor gait incorporated with GTDA is demonstrated to significantly outperform the existing appearance-based methods.
Efficient kNN Classification With Different Numbers of Nearest Neighbors

<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> value (even though set by experts) to all test samples. Previous solutions assign different <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> values to different test samples by the cross validation method but are usually time-consuming. This paper proposes a kTree method to learn different optimal <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> values for different test/new samples, by involving a training stage in the kNN classification. Specifically, in the training stage, kTree method first learns optimal <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> values for all training samples by a new sparse reconstruction model, and then constructs a decision tree (namely, kTree) using training samples and the learned optimal <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> values. In the test stage, the kTree fast outputs the optimal <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> value for each test sample, and then, the kNN classification can be conducted using the learned optimal <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> value and all training samples. As a result, the proposed kTree method has a similar running cost but higher classification accuracy, compared with traditional kNN methods, which assign a fixed <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> value to all test samples. Moreover, the proposed kTree method needs less running cost but achieves similar classification accuracy, compared with the newly kNN methods, which assign different <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> values to different test samples. This paper further proposes an improvement version of kTree method (namely, k*Tree method) to speed its test stage by extra storing the information of the training samples in the leaf nodes of kTree, such as the training samples located in the leaf nodes, their kNNs, and the nearest neighbor of these kNNs. We call the resulting decision tree as k*Tree, which enables to conduct kNN classification using a subset of the training samples in the leaf nodes rather than all training samples used in the newly kNN methods. This actually reduces running cost of test stage. Finally, the experimental results on 20 real data sets showed that our proposed methods (i.e., kTree and k*Tree) are much more efficient than the compared methods in terms of classification tasks.