nils-herrmann/clean_scirepeval huggingface dataset:
An efficient graph theoretic approach to video scene clustering

In this paper, a graph theoretic approach is presented for video scene clustering in sports and news videos. In the approach, video shots are grouped into clusters of similar scenes based on shot color attributes. First, the similarity between video shots is measured by shot color histogram intersection. To obtain scene likeness matrix in a maximum-a-posterior (MAP) probability manner, a thresholding method is proposed on shot similarity matrix. Then, a graph is constructed based on the scene likeness that similar shots have an edge between them. Based on the constructed graph, a graph partitioning method is proposed to cluster video shots into different scenes such that the connectivity of video shots within one cluster is higher than that between different clusters. The advantage of the graph partitioning method is that the cluster number need not to be known as a prior. The graph partitioning method is compared with conventional k-means clustering method; in which the cluster number is determined by the cluster validity measure. The main contributions of the paper lie in the formulation of video scene clustering to a graph partitioning problem, and the comparison with the conventional k-means clustering method. Experimental results are presented to show the effectiveness and efficiency of the proposed graph theoretic approach.
Efficient clustering of face sequences with application to character-based movie browsing

Human face has emerged as a useful feature in recent advances in semantic-based video analysis. Due to the large variation of facial poses in videos, conventional face recognition methods for authentication and identification generally experience difficulties in this particular application domain. Based on the recently proposed affinity propagation algorithm, we present in this paper a new method to match and cluster human faces with large pose variations. We also devise a novel approach to exploring the storyline of character(s) of particular interest for efficient movie navigation and browsing. Experimental results are reported to show the efficacy of our method.
Adaptive dual-cross search algorithm for block-matching motion estimation

In this paper, we present two new and efficient block-matching algorithm (BMAs) for fast motion estimation used in motion-compensated video compression. The proposed algorithms are improvement of existing BMAs and consist of three effective steps: 1) Initial search center prediction, 2) early search termination, and 3) dual-cross pattern search. Extensive simulation results and comparative analysis have shown that the proposed algorithms outperform conventional algorithms, like the three-step search, orthogonal search and diamond search, as well as the newly proposed algorithms, like the hexagonal search and adaptive rood pattern search, in terms of both video peak signal-to-noise ratio and the number of checking points are evaluated.
Context-aware graph-based analysis for detecting anomalous activities

This paper proposes a context-aware, graph-based approach for identifying anomalous user activities via user profile analysis, which obtains a group of users maximally similar among themselves as well as to the query during test time. The main challenges for the anomaly detection task are: (1) rare occurrences of anomalies making it difficult for exhaustive identification with reasonable false-alarm rate, and (2) continuously evolving new context-dependent anomaly types making it difficult to synthesize the activities apriori. Our proposed query-adaptive graph-based optimization approach, solvable using maximum flow algorithm, is designed to fully utilize both mutual similarities among the user models and their respective similarities with the query to shortlist the user profiles for a more reliable aggregated detection. Each user activity is represented using inputs from several multi-modal resources, which helps to localize anomalies from time-dependent data efficiently. Experiments on public datasets of insider threats and gesture recognition show impressive results.
Error Inhomogeneity of Wavelet Image Compression

Despite the popularity of wavelet-based image compression, its error inhomogeneity-the error that is different for even and odd pixel locations, has not been previously analyzed and formally addressed. The difference on PSNR performance can be substantial, up to 3.4 dB for some images and compression ratios. In this paper, we show that the error inhomogeneity is caused by asymmetrical filtering of quantization errors after the upsampling step in wavelet synthesis process. We also develop a model that also allows predicting the amount of inhomogeneity for a given wavelet. Furthermore, we show how to redesign wavelet filters to reduce the error inhomogeneity.
Regularized dequantization for image copies compressed with different quantization parameters

A single source image can be compressed into many different copies with different quantization parameters. An end user may have access to a few of such copies (e.g., over the Internet), but would like to obtain one that has superior quality to any available copy. We propose in this paper to improve the reconstruction quality by using regularization and refining the narrowed quantization constraint set (NQCS) from such image copies. Since the size of NQCS is generally large, we also discuss the estimation of initial images for regularization by applying proper probability models. Experimental results show that the proposed scheme can consistently improve the quality of the reconstructed images in both subjective and objective terms.
Adaptive maximum margin criterion for image classification

We propose in this paper a novel adaptive maximum margin criterion (AMMC) method for image classification. While a large number of discriminant analysis algorithms have been proposed in recent years, most of them consider an equal importance of each training sample and ignore the different contributions of these samples to learn the discriminative feature subspace for classification. Motivated by the fact that some training samples are more effectual in learning the low-dimensional feature space than other samples, we propose using different weights to characterize the different contributions of the training samples and incorporate such weighting information into the popular maximum margin criterion algorithm to devise the corresponding AMMC for image classification. Moreover, we extend the proposed MMC algorithm to the semi-supervised case, namely, semi-supervised adaptive maximum margin criterion (SAMMC), by making use of both labeled and unlabeled samples to further improve the classification performance. Experimental results are presented to demonstrate the efficacy of the proposed methods.
Motion vector re-estimation for fractional-scale video transcoding

In video transcoding, the compressed video is often converted to a lower resolution in order to reduce the bit rate. One way to achieve this is to first decompress the compressed video, down-sample each frame, and then re-compress the down-scaled frame sequences. This method can obtain high-quality compressed video bit-streams; however, it involves motion vector estimation that is computationally complex. Recently, some transcoding techniques have been proposed to compute the motion vectors for a compressed video with lower resolution using the motion vectors directly from the original compressed video. These techniques, however, cannot be used in arbitrary-size video down-scaling, e.g for converting CIF (Common Intermediate Format) video to sub-QCIF (Quarter CIF) video or QCIF to sub-QCIF. In this paper, we propose a video transcoding method that can estimate the motion vectors for arbitrary-size down-scaled video. It can provide video quality that is comparable to full-search motion estimation but with very low computational complexity.
Locality repulsion projections for image-to-set face recognition

Conventional face recognition usually assumes that both the training and test phases employ the same form of data in a face recognition system. In many real world face recognition applications such as e-passport and ID card identification, it is common to have only a single sample per person enrolled or recorded in these systems because it is generally difficult to collect additional samples for training. In the testing phase, however, the probe samples are usually captured on spot and it is possible to collect a number of face samples for recognition. This problem is defined as image-to-set face recognition in this paper, which is essentially different from most existing image-to-image or set-to-set face recognition problems. Specifically, we propose in this paper a new locality repulsion projections (LRP) method to address this problem. Motivated by the fact that interclass face samples with higher similarity usually lie in a locality and are more easily misclassified than those with lower similarity, we aim to learn a mapping to project the original face samples onto a low-dimensional feature subspace such that samples lying in a locality are repulsed and more discriminative information can be exploited for recognition. To better characterize the similarity between a gallery sample and a testing set, we further propose a reconstruction-based point-to-set similarity measure to identify the unlabeled subjects. Experimental results on two widely used face databases are presented to demonstrate the efficacy of the proposed method.
Reversing Demosaicking and Compression in Color Filter Array Images

An alternative processing chain of digital still camera (DSC) has been proposed by moving the compression process before demosaicking to avoid additional data redundancy introduced by the demosaicking process. Recent empirical studies have shown that the alternative processing chain can actually outperform the conventional one in terms of image quality at low compression ratios. To provide theoretically sound basis for such conclusion, we propose analytical models for the reconstruction errors of the two processing chains. The models developed confirm the results of existing empirical studies and also allows performance predictions for more advanced compression and demosaicking methods, thus providing important cues for development in this area.
Energy-Efficient Resource Allocation and Scheduling for Multicast of Scalable Video Over Wireless Networks

In this paper, we investigate optimal resource allocation and scheduling for scalable video multicast over wireless networks. The wireless video multicasting is a best-effort service which has limited transmission energy and channel access time. To cater for multi-resolution videos to heterogeneous clients and for channel adaptation, we adopt scalable video coding (SVC) with spatial, temporal and quality scalabilities. Our scalable video multicast system consists of a channel probing stage to gather the channel state information and a transmission stage to multicast videos to clients. We formulate the optimal resource allocation problem by maximizing the video quality of the clients subject to transmission energy and channel access constraints. We show that the problem is a joint optimization of the selection of modulation and coding scheme (MCS), and the transmission power allocation. By imposing a quality-of-service (QoS) constraint on the packet loss rate, we simplify the original problem to a binary knapsack problem which can be solved by a dynamic programming approach. Specifically, we first propose a multicast scheduling scheme based on the quality impact of each SVC layer. Guided by the content-aware multicast scheduling, we optimize the resource allocation for each SVC layer sequentially. Solution at each step takes into account of the channel condition, remaining resources, and client requirements. The proposed scheme is of linear complexity and leads to the maximized video quality for the admitted clients, while satisfying the energy budget and channel access constraints. Experiment results demonstrate that our scheme achieves notable video quality improvements for multicast clients, when compared to the state-of-the-art video multicast method.
Robust Feature Set Matching for Partial Face Recognition

Over the past two decades, a number of face recognition methods have been proposed in the literature. Most of them use holistic face images to recognize people. However, human faces are easily occluded by other objects in many real-world scenarios and we have to recognize the person of interest from his/her partial faces. In this paper, we propose a new partial face recognition approach by using feature set matching, which is able to align partial face patches to holistic gallery faces automatically and is robust to occlusions and illumination changes. Given each gallery image and probe face patch, we first detect key points and extract their local features. Then, we propose a Metric Learned Extended Robust Point Matching (MLERPM) method to discriminatively match local feature sets of a pair of gallery and probe samples. Lastly, the similarity of two faces is converted as the distance between two feature sets. Experimental results on three public face databases are presented to show the effectiveness of the proposed approach.
Video organization: Near-Duplicate Video clustering

It is not uncommon to see several videos of almost identical content on the internet. These near duplicates, coupled with the sheer number of videos, pose a big challenge to the effective organization of video clips online. We propose an adaptive classification approach to detect near-duplicate versions, and an integrated voting strategy to group clusters and to elect a representative for each cluster. Our proposed methods are based on our observation that near-duplicate videos usually span a small, albeit variable area in the feature space, while videos of different contents are scattered far apart. The classification method aims to select a suitable threshold by maximizing the margin for each video sequence in the similarity space, and the voting scheme focuses on merging subsets with mutual information based on neighbor information and inverted indices. Experimental results on an unconstrained web dataset including over 10000 videos demonstrate the efficacy of the proposed methods.
Media-rich interactive mobile learning assistant

The proposed system aims to provide useful tool kits to create more effective and interactive learning environment through the use of mobile devices. This system facilitates a flexible way in delivering the course contents and interaction among the instructor and students. It allows students to take notes or to submit answers and feedbacks during a lecture in media formats customized to the users' needs and preferences. Efficient delivery schemes are proposed for media content delivery to optimize system and network resources. Furthermore, useful statistics together with location-based analysis are provided to aid the instructor in accessing the current level of understanding or attention of the students.
A probabilistic reasoning approach to closed-room people monitoring

We propose a novel probabilistic reasoning approach to recognizing people entering and leaving a closed room in the work place or living environment. In our method people are represented using a low-level color feature, based on which optimal recognition is carried out by exploiting the temporal correlation among the sequence of observations of people. Optimality is achieved by maximizing a joint posterior probability. Experimental results are presented to show the efficacy of the proposed approach.
Image Denoising Using Optimal Color Space Projection

Denoising of color images can be improved by exploiting strong correlation between high-frequency content of different color component. We show that for typical color images high correlation also means similarity, and propose to exploit this property using an optimal luminance/color-difference space projection. Experimental results confirm that denoising in the proposed color space yields superior performance, both in PSNR and visual quality sense, compared to that of existing solutions
Adaptive color filter array demosaicing with artifact suppression

Artifacts such as zipper effect and false color degrade the quality of the recovered images in color filter array demosaicing. To suppress these artifacts and recover high-quality images, we propose a hybrid demosaicing method in this paper. The proposed method consists of two successive processes: a reconstruction process, which adopts a number of spatial adaptive schemes greatly reducing zipper effects around edges regions, and refinement process, which utilizes adaptive median filtering as well as iterative correction in color difference planes to suppress false color. Experimental results show that the proposed method outperforms existing state-of-the-art methods, both visually and quantitatively, on a large variety of test images.
Discriminative multi-manifold analysis for face recognition from a single training sample per person

Conventional appearance-based face recognition methods usually assume there are multiple samples per person (MSPP) available during the training phase for discriminative feature extraction. In many practical face recognition applications such as law enhancement, e-passport and ID card identification, this assumption, however, may not hold as there is only a single sample per person (SSPP) enrolled or recorded in these systems. Many popular face recognition methods fail to work well in this scenario because there are not enough samples for discriminant learning. To address this problem, we propose in this paper a novel discriminative multi-manifold analysis (DMMA) method by learning discriminative features from image patches. First, we partition each enrolled image into several non-overlapping patches to form an image set for each sample per person. Then, we formulate the SSPP face recognition as a manifold-manifold matching problem and learn multiple DMMA feature spaces to maximize the manifold margins of different persons. Lastly, we propose a reconstruction-based manifold-manifold distance to identify the unlabeled subjects. Experimental results on three widely used face databases are presented to demonstrate the efficacy of the proposed approach.
Probabilistic approach to K-nearest neighbor video retrieval

In this paper we propose a probabilistic approach to retrieve video clips similar to a given query video clip. In our approach the video clips are partitioned into video segments based on their content homogeneity, and video segments in the database are connected to construct candidate clips and compared with the query clip for their similarity (or distance) during the query process. An efficient scheme is developed to estimate the probability density functions of the distances between the candidate clips and query clip, and based on these density functions, two methods are devised to reduce the number of candidate clips for comparison to speed up the retrieval process. Experimental results show that our proposed approach can notably speed up the retrieval of similar video clips, while maintaining high retrieval accuracy.
Nearest Feature Space Analysis for Classification

We propose in this letter two new subspace learning methods, called nearest feature space analysis (NFSA) and discriminant nearest feature space analysis (DNFSA), for pattern classification. While many subspace learning algorithms have been proposed in recent years, most of them apply the conventional nearest neighbor (NN) metric to derive the subspace and may not effectively characterize the geometrical information of the samples, especially when the number of training samples per class is limited. In this paper, we propose using the nearest feature space (NFS) metric to seek a NFSA subspace to improve the discriminating power of the subspace for classification. To further enhance the discriminative power of NFSA, we also propose a new DNFSA method to minimize the within-class feature space (FS) distances and maximize the between-class FS distances simultaneously in the derived subspace. Experimental results on face and facial expression recognition are presented to demonstrate the efficacy of the proposed methods.
Cost-Sensitive Subspace Analysis and Extensions for Face Recognition

Conventional subspace-based face recognition methods seek low-dimensional feature subspaces to achieve high classification accuracy and assume the same loss from different types of misclassification. This assumption, however, may not hold in many practical face recognition systems as different types of misclassification could lead to different losses. Motivated by this concern, this paper proposes a cost-sensitive subspace analysis approach for face recognition. Our approach uses a cost matrix specifying different costs corresponding to different types of misclassifications, into two popular and widely used discriminative subspace analysis methods and devises the cost-sensitive linear discriminant analysis (CSLDA) and cost-sensitive marginal fisher analysis (CSMFA) methods, to achieve a minimum overall recognition loss by performing recognition in these learned low-dimensional subspaces. To better exploit the complementary information from multiple features for improved face recognition, we further propose a multiview cost-sensitive subspace analysis approach by seeking a common feature subspace to fuse multiple face features to improve the recognition performance. Extensive experimental results demonstrate the effectiveness of our proposed methods.
Fast motion re-estimation for arbitrary downsizing video transcoding using H.264/AVC standard

Downsizing video transcoding is a technique for adapting the bit rate or frame size of a preceded video to suit better the constraints of transmission networks and target applications. To sustain good video quality at reduced bit rates, we have previously proposed H.263-based transcoding techniques for downsizing preceded videos by an arbitrary factor. To fully exploit the advantages of video transcoding, we investigate and propose in this paper an effective and fast motion re-estimation method for arbitrary downsizing video transcoding using the newly developed H.264/AVC standard. Specifically, as H.264/AVC allows more macroblock partitions and reference frames for inter-frame motion-compensated prediction, we propose an area-weighted vector median filter to estimate the new required motion vectors from the motion vectors existing in a preceded video. Experimental results show that the proposed method can achieve a notable improvement in both subjective and objective video quality for transcoding preceded H.263 or H.264 videos at reduced bit rates and frame sizes.
An efficient multicast algorithm for the scalable extension of H.264/AVC over IEEE 802.11 WLANs

We present an efficient resource allocation algorithm for scalable video multicast over wireless links with limited channel access time and transmit energy budget. We first introduce a multicast architecture for the scalable extension of H.264/AVC based video delivery over IEEE 802.11 WLANs. We show that the resource allocation problem is a mixed-integer program for which we propose an efficient solution. Our algorithm explicitly takes into account of relevant factors, such as quality demands of multicast clients, wireless channel conditions, packet length, and different priorities of SVC layers. We maximize the utility of video streaming whilst satisfying channel access and energy constraints. The proposed solution jointly considers PHY mode selection and SNR allocation. Our simulation results demonstrate that the proposed algorithm achieves significant performance improvements for multicast clients in resource-constrained wireless networks.