nils-herrmann/clean_scirepeval huggingface dataset:
Robust semantic concept detection in large video collections

With explosive amounts of video data emerging from the Internet, automatic video concept detection is becoming very important and has been received great attention. However, reported approaches mainly suffer from low identification accuracy and poor robustness over different concepts. One of the main reason is that the existing approaches typically isolate the video signature generation from the process of classifier training. Also, very few approaches consider effects of multiple video features. The paper describes a novel approach fusing different information from diverse knowledge sources to facilitate effective video concept detection. The system is designed based on CM∗F scheme ,  and its basic architecture contains two core components including 1) CM∗F based video signature generation scheme and 2) CM∗F based video concept detector. To evaluate the approach proposed, an extensive experimental study on two large video databases has been carried out. The results demonstrate the superiority of the method in terms of effectiveness and robustness.
Bayesian Tensor Approach for 3-D Face Modeling

Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D faces naturally form a set of second-order tensors-one modality for identity and the other for expression. The number of these second-order tensors is three times of that of the vertices for 3-D face modeling. As for algorithms, Bayesian data modeling, which is a natural data analysis tool, has been widely applied with great success; however, it works only for vector data. Therefore, there is a gap between tensor-based representation and vector-based data analysis tools. Aiming at bridging this gap and generalizing conventional statistical tools over tensors, this paper proposes a decoupled probabilistic algorithm, which is named Bayesian tensor analysis (BTA). Theoretically, BTA can automatically and suitably determine dimensionality for different modalities of tensor data. With BTA, a collection of 3-D faces can be well modeled. Empirical studies on expression retargeting also justify the advantages of BTA.
Complementary Projection Hashing

Recently, hashing techniques have been widely applied to solve the approximate nearest neighbors search problem in many vision applications. Generally, these hashing approaches generate 2^c buckets, where c is the length of the hash code. A good hashing method should satisfy the following two requirements: 1) mapping the nearby data points into the same bucket or nearby (measured by the Hamming distance) buckets. 2) all the data points are evenly distributed among all the buckets. In this paper, we propose a novel algorithm named Complementary Projection Hashing (CPH) to find the optimal hashing functions which explicitly considers the above two requirements. Specifically, CPH aims at sequentially finding a series of hyper planes (hashing functions) which cross the sparse region of the data. At the same time, the data points are evenly distributed in the hyper cubes generated by these hyper planes. The experiments comparing with the state-of-the-art hashing methods demonstrate the effectiveness of the proposed method.
Relay selection for wireless communications against eavesdropping: a security-reliability trade-off perspective

This article examines secrecy coding aided wireless communications from a source to a destination in the presence of an eavesdropper from an SRT perspective. Explicitly, security is quantified in terms of the intercept probability experienced at the eavesdropper, while the outage probability encountered at the destination is used to measure transmission reliability. We characterize the SRT of conventional direct transmission from the source to the destination and show that if the outage probability is increased, the intercept probability decreases, and vice versa. We first demonstrate that the employment of relay nodes for assisting source-destination transmission is capable of defending against eavesdropping, followed by quantifying the benefits of SRS as well as MRS schemes. More specifically, in the SRS scheme, only the single "best" relay is selected for forwarding the source signal to the destination, whereas the MRS scheme allows multiple relays to participate in this process. It is illustrated that both the SRS and MRS schemes achieve a better SRT than conventional direct transmission, especially upon increasing the number of relays. Numerical results also show that, as expected, MRS outperforms SRS in terms of its SRT. Additionally, we present some open challenges and future directions for wireless relay aided physical-layer security.
Negative Samples Analysis in Relevance Feedback

Recently, relevance feedback (RF) in content-based image retrieval (CBIR) has been implemented as an online binary classifier to separate the positive samples from the negative samples, where both sets of samples are labeled by the user. In many applications, it is reasonable to assume that all the positive samples are alike and thus that the region of the feature space occupied by the positive samples can be described by a single hypersurface. However, for the negative samples, previous RF methods either treat each one of the negative samples as an isolated point or assume the whole negative set can be described by a single convex hypersurface. In this paper, we argue that these treatments of the negative samples are not sound. Our belief is all positive samples are included in a set and the negative samples split into a small number of subsets, each one of which has a simple distribution. Therefore, we first cluster the negative samples into several groups; for each such negative group, we build a marginal convex machine (MCM) subclassifier between it and the single positive group which results in a series of subclassifiers. These subclassifiers are then incorporated into a biased MCM (BMCM) for RF. Experiments were carried out to prove the advantages of BMCM-based RF over previous methods for RF
Local semi-supervised regression for single-image super-resolution

In this paper, we propose a local semi-supervised learning-based algorithm for single-image super-resolution. Different from most of example-based algorithms, the information of test patches is considered during learning local regression functions which map a low-resolution patch to a high-resolution patch. Localization strategy is generally adopted in single-image super-resolution with nearest neighbor-based algorithms. However, the poor generalization of the nearest neighbor estimation decreases the performance of such algorithms. Though the problem can be fixed by local regression algorithms, the sizes of local training sets are always too small to improve the performance of nearest neighbor-based algorithms significantly. To overcome the difficulty, the semi-supervised regression algorithm is used here. Unlike supervised regression, the information about test samples is considered in semi-supervised regression algorithms, which makes the semi-supervised regression more powerful. Noticing that numerous test patches exist, the performance of nearest neighbor-based algorithms can be further improved by employing a semi-supervised regression algorithm. Experiments verify the effectiveness of the proposed algorithm.
Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification.

Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance. Generally, in the field of multi-view learning, these algorithms construct informative graph for each view, on which the following clustering or classification procedure are based. However, in many real-world data sets, original data always contain noises and outlying entries that result in unreliable and inaccurate graphs, which cannot be ameliorated in the previous methods. In this paper, we propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously. The obtained optimal graph can be partitioned into specific clusters directly. Moreover, our model can allocate ideal weight for each view automatically without explicit weight definition and penalty parameters. An efficient algorithm is proposed to optimize this model. Extensive experimental results on different real-world data sets show that the proposed model outperforms other state-of-the-art multi-view algorithms.
High-Order Energies for Stereo Segmentation

In this paper, we propose a novel segmentation approach for stereo images using the high-order energy optimization, which utilizes the disparity maps and statistical information of stereo images to enrich the high-order potential functions. To the best of our knowledge, our approach is the first one to formulate the problem of stereo segmentation as a high-order energy optimization problem, which simultaneously segments the foreground objects in left and right images using the proposed high-order potential function. A new method for designing the penalty function in our high-order term is proposed by the corresponding pixels and their neighboring pixels between left and right images. The relationships of stereo correspondence by disparity maps are further employed to enhance the connections between the left and right stereo images. Experimental results demonstrate that the proposed approach can effectively improve the performance of two kinds of stereo segmentation, including the automatic saliency-aware stereocut and the interactive stereo segmentation with user scribbles.
A set of novel textural features based on 3D co-occurrence matrix for content-based image retrieval

This paper presents a set of novel texture features for content-based image retrieval (CBIR). CBIR require new algorithms for the automated extraction and indexing of salient image features, while texture features provide one important cue for the visual perception and discrimination of image content, so they are always one of the most popular representations in CBIR. The proposed features are based on the 3D co-occurrence matrix, and preliminary results show they are much better than traditional texture features. In this paper, experiments are made on an image database with 25000 images.
Accelerating Vehicle Detection in Low-Altitude Airborne Urban Video

The limitation of the existing methods of traffic data collection is that they rely on techniques that are strictly local in nature. The airborne system in unmanned aircrafts provides the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. Most of the techniques used in stationary platforms cannot perform well in this situation. A new and efficient method based on Bayes model is proposed in this paper. This method can be divided into two stages, attention focus extraction and vehicle classification. Experimental results demonstrated that, compared with other representative algorithms, our method obtained better performance with higher detection rate, lower false positive rate and faster detection speed.
Blind Image Quality Assessment via Deep Learning

This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, lots of learning-based IQA models are proposed by analyzing the mapping from the images to numerical ratings. However, the learnt mapping can hardly be accurate enough because some information has been lost in such an irreversible conversion from the linguistic descriptions to numerical scores. In this paper, we propose a blind IQA model, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison. Images are represented by natural scene statistics features. A discriminative deep model is trained to classify the features into five grades, corresponding to five explicit mental concepts, i.e., excellent, good, fair, poor, and bad. A newly designed quality pooling is then applied to convert the qualitative labels into scores. The classification framework is not only much more natural than the regression-based models, but also robust to the small sample size problem. Thorough experiments are conducted on popular databases to verify the model's effectiveness, efficiency, and robustness.
Image Annotation by Multiple-Instance Learning With Discriminative Feature Mapping and Selection

Multiple-instance learning (MIL) has been widely investigated in image annotation for its capability of exploring region-level visual information of images. Recent studies show that, by performing feature mapping, MIL can be cast to a single-instance learning problem and, thus, can be solved by traditional supervised learning methods. However, the approaches for feature mapping usually overlook the discriminative ability and the noises of the generated features. In this paper, we propose an MIL method with discriminative feature mapping and feature selection, aiming at solving this problem. Our method is able to explore both the positive and negative concept correlations. It can also select the effective features from a large and diverse set of low-level features for each concept under MIL settings. Experimental results and comparison with other methods demonstrate the effectiveness of our approach.
Toward Multiscreen Social TV with Geolocation-Aware Social Sense

The increasing popularity of social interactions and geotagged, user-generated content has transformed the television viewing experience from laid-back video watching behavior into a "lean-forward"' socially engaged experience. This article describes a multiscreen, social TV system integrated with social sense via a second screen as a novel paradigm for content consumption. This new application is built upon the authors' cloud-centric media platform, which provides on-demand virtual machines for content platform services, including media distribution, storage, and processing. The media platform is also integrated with a Big Data social platform that crawls and mines social data related to the media content. Specifically, this new social TV approach consists of three key subsystems: interactive TV, social sense, and multiscreen orchestration. Interactive TV implements a cloud-based, social TV system, offering rich social features; social sense discovers the geolocation-aware public perception and knowledge related to the media content; and multiscreen orchestration provides an intuitive and user-friendly human-computer interface to combine the two other subsystems, fusing the TV viewing experience with social perception. The authors have built a proof-of-concept demo over a private cloud at the Nanyang Technological University (NTU), Singapore. Feature verification and performance comparisons demonstrate the feasibility and effectiveness of the proposed approach in transforming the TV viewing experience.
Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks

Eye movements in the case of freely viewing natural scenes are believed to be guided by local contrast, global contrast, and top-down visual factors. Although a lot of previous works have explored these three saliency cues for several years, there still exists much room for improvement on how to model them and integrate them effectively. This paper proposes a novel computation model to predict eye fixations, which adopts a multiresolution convolutional neural network (Mr-CNN) to infer these three types of saliency cues from raw image data simultaneously. The proposed Mr-CNN is trained directly from fixation and nonfixation pixels with multiresolution input image regions with different contexts. It utilizes image pixels as inputs and eye fixation points as labels. Then, both the local and global contrasts are learned by fusing information in multiple contexts. Meanwhile, various top-down factors are learned in higher layers. Finally, optimal combination of top-down factors and bottom-up contrasts can be learned to predict eye fixations. The proposed approach significantly outperforms the state-of-the-art methods on several publically available benchmark databases, demonstrating the superiority of Mr-CNN. We also apply our method to the RGB-D image saliency detection problem. Through learning saliency cues induced by depth and RGB information on pixel level jointly and their interactions, our model achieves better performance on predicting eye fixations in RGB-D images.
A unifying framework for spectral analysis based dimensionality reduction

Past decades, numerous spectral analysis based algorithms have been proposed for dimensionality reduction, which plays an important role in machine learning and artificial intelligence. However, most of these existing algorithms are developed intuitively and pragmatically, i.e., on the base of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide some a systematic framework for understanding the common properties and intrinsic differences in the algorithms. In this paper, we propose such a framework, i.e., ldquopatch alignmentrdquo, which consists of two stages: part optimization and whole alignment. With the proposed framework, various algorithms including the conventional linear algorithms and the manifold learning algorithms are reformulated into a unified form, which gives us some new understandings on these algorithms.
LDFT-Based Watermarking Resilient to Local Desynchronization Attacks

Up to now, a watermarking scheme that is robust against desynchronization attacks (DAs) is still a grand challenge. Most image watermarking resynchronization schemes in literature can survive individual global DAs (e.g., rotation, scaling, translation, and other affine transforms), but few are resilient to challenging cropping and local DAs. The main reason is that robust features for watermark synchronization are only globally invariable rather than locally invariable. In this paper, we present a blind image watermarking resynchronization scheme against local transform attacks. First, we propose a new feature transform named local daisy feature transform (LDFT), which is not only globally but also locally invariable. Then, the binary space partitioning (BSP) tree is used to partition the geometrically invariant LDFT space. In the BSP tree, the location of each pixel is fixed under global transform, local transform, and cropping. Lastly, the watermarking sequence is embedded bit by bit into each leaf node of the BSP tree by using the logarithmic quantization index modulation watermarking embedding method. Simulation results show that the proposed watermarking scheme can survive numerous kinds of distortions, including common image-processing attacks, local and global DAs, and noninvertible cropping.
Joint Content Replication and Request Routing for Social Video Distribution Over Cloud CDN: A Community Clustering Method

The increasing popularity of online social networks (OSNs) has been transforming the dissemination pattern of social video contents. We can utilize the social information propagation pattern to improve the efficiency of social video distribution. In this paper, motivated by the social community classification, we present a social video replication and user request dispatching mechanism in the cloud content delivery network architecture to reduce the system operational cost, while guaranteeing the averaged service latency. Specifically, we first present a community classification method that clusters social users with social relationships, close geolocations, and similar video watching interests into various communities. Then, we conduct a large-scale measurement on a real OSN system to study the diversities of social video propagation and the effectiveness of our communities on smoothing the diversity. Finally, we propose the community-based video replication and request dispatching strategy and formulate it as a constrained optimization problem. Based on a stochastic optimization framework, we derive an online solution and rigorously prove the optimality. We evaluate our algorithm on a real trace under realistic settings and demonstrate that our algorithm can reduce the monetary cost by 30% against traditional approaches with the same service latency.
Structured-Patch Optimization for Dense Correspondence

This paper presents a new method to compute the dense correspondences between two images by using the energy optimization and the structured patches. In terms of the property of the sparse feature and the principle that nearest sub-scenes and neighbors are much more similar, we design a new energy optimization to guide the dense matching process and find the reliable correspondences. The sparse features are also employed to design a new structure to describe the patches. Both transformation and deformation with the structured patches are considered and incorporated into an energy optimization framework. Thus, our algorithm can match the objects robustly in complicated scenes. Finally, a local refinement technique is proposed to solve the perturbation of the matched patches. Experimental results demonstrate that our method outperforms the state-of-the-art matching algorithms.
Sparse representation for blind image quality assessment

Blind image quality assessment (BIQA) is an important yet difficult task in image processing related applications. Existing algorithms for universal BIQA learn a mapping from features of an image to the corresponding subjective quality or divide the image into different distortions before mapping. Although these algorithms are promising, they face the following problems: (1) they require a large number of samples (pairs of distorted image and its subjective quality) to train a robust mapping; (2) they are sensitive to different datasets; and (3) they have to be retrained when new training samples are available. In this paper, we introduce a simple yet effective algorithm based upon the sparse representation of natural scene statistics (NSS) feature. It consists of three key steps: extracting NSS features in the wavelet domain, representing features via sparse coding, and weighting differential mean opinion scores by the sparse coding coefficients to obtain the final visual quality values. Thorough experiments on standard databases show that the proposed algorithm outperforms representative BIQA algorithms and some full-reference metrics.
Incremental Learning With Saliency Map for Moving Object Detection

Moving object detection is a key to intelligent video analysis. On the one hand, what moves are not only interesting objects but also noise and cluttered background. On the other hand, moving objects without rich texture are prone to not be detected. Therefore, there are undesirable false alarms and missed alarms in the results of many algorithms of moving object detection. To reduce the false alarms and missed alarms, in this paper we propose to incorporate a saliency map into an incremental subspace analysis framework in which the saliency map makes the estimated background have less of a chance than the foreground (i.e., moving objects) to contain salient objects. The proposed objective function systematically takes into account the properties of sparsity, low rank, connectivity, and saliency. An alternative minimization algorithm is proposed to seek the optimal solutions. The experimental results on both the Perception Test Images Sequences data set and Wallflower data set demonstrate that the proposed method is effective in reducing false alarms and missed alarms.