nils-herrmann/clean_scirepeval huggingface dataset:
Feedback stabilization of MIMO systems in the presence of stochastic network uncertainties and delays

The purpose of this paper is to study stabilization problem of linear time-invariant systems subject to stochastic multiplicative uncertainties and time delays. We consider a structured multiplicative perturbation which consists of static, zero-mean stochastic processes and we assess the stability of system based on mean-square criteria. The mean-square stabilization problem for multi-input multi-output systems generally requires solving an optimization problem involving the spectral radius of a certain closed loop transfer function matrix. This problem in general is non-convex and by and large unresolved, only approximate solutions are available based on numerical algorithms resembling to the D-K iteration for μ-synthesis. Our main contributions include the fundamental conditions, both necessary and sufficient, which insure that the multi-input multi-output minimum phase systems can be stabilized by output feedback in the mean-square sense. We provide a complete, computationally efficient solution in the form of a generalized eigenvalue problem readily solvable by means of linear matrix inequality optimization. For conceptual insights, limiting cases are analyzed in depth to characterize and quantify explicitly how the directions of unstable poles may affect the mean-square stabilizability of multi-input multi-output systems.
A hierarchical approach to story segmentation of large broadcast news video corpus

A multi-modal two-level framework for news story segmentation was proposed in Chaisorn et al. (2002). This paper presents our extended work scaled to cope with a large news video corpus used in TRECVID 2003 evaluation. We divided our system into two levels: the shot level that classifies input video shots into one of the predefined categories using a hybrid of heuristic and learning based approaches; and story level that performs story segmentation using the HMM framework based on the output of shot level and other temporal features. A heuristic rule-based technique is then employed to classify each detected story into "news" or "miscellaneous". We evaluated our system on over 120 hours of news video and showed that our system could achieve an accuracy of more than 77%. Our system came first in the TRECVID 2003 story segmentation task
Construction of a linear unbiased diffeomorphic probabilistic liver atlas from CT images

The construction of probabilistic liver atlases has received little attention in the past. Existing methods are based on landmarks and are sensitive to their choices and placements. We propose an iterative landmark-free method based on dense volumes to construct linear unbiased diffeomorphic probabilistic atlases from liver CT images. The linear averaging of the transformed images is set as the common target space followed by pairwise diffeomorphic registrations to warp all images to the target using a recent-proposed efficient deformation approach during each iteration cycle. Iterative pairwise registrations are directly used to handle possible large deformations without the need for an extra step to remove global deformations such as the use of affine transformations in traditional methods. Compared with those approaches estimating the unbiased atlas and the transformations groupwise simultaneously, the current method is more efficient. The efficiency and the convergence of our method have been demonstrated experimentally by validation using 25 CT liver sets.
Optimal Tracking Performance of a Linear System with a Quantized Control Input

This paper studies the optimal tracking performance of a linear system with a quantized control signal. The plant under consideration is linear time-invariant (LTI) stable and the reference signal in the tracking problem is a step signal. The tracking performance is measured by the energy of the error between the output of the plant and the reference. To achieve asymptotical tracking, a new quantization scheme is proposed. This scheme includes two parts: one is quantized steady-state control signal transmitted to the plant at initial time and the other is a logarithmic quantizer which quantizes the error between the control signal and its steady-state value. The logarithmic quantization error is assumed to be a product of the original signal and a white noise with a uniform distribution over a given range. The best attainable tracking performance is obtained, in terms of characteristics of the given plant and the quantizer. This result gives a clear answer to a fundamental question in networked control system: How large quantization error is allowed for a step tracking problem with a given tracking error energy level in an averaged sense?
Image Annotation by Latent Community Detection and Multikernel Learning

Automatic image annotation is an attractive service for users and administrators of online photo sharing websites. In this paper, we propose an image annotation approach that exploits latent semantic community of labels and multikernel learning (LCMKL). First, a concept graph is constructed for labels indicating the relationship between the concepts. Based on the concept graph, semantic communities are explored using an automatic community detection method. For an image to be annotated, a multikernel support vector machine is used to determine the image's latent community from its visual features. Then, a candidate label ranking based approach is determined by intracommunity and intercommunity ranking. Experiments on the NUS-WIDE database and IAPR TC-12 data set demonstrate that LCMKL outperforms some state-of-the-art approaches.
Rank-aware graph fusion with contextual dissimilarity measurement for image retrieval

In content based image retrieval, due to the diverse variations of visual content, the retrieval performance from single feature or retrieval method is usually limited. Generally, better retrieval results are obtained by combining multiple visual features. In this work, we propose a rank-aware graph fusion scheme to fuse the results from multiple retrieval methods. We first refine the initial ranking result by enhancing the neighbor reversibility of database images. Then, we adopt a graph structure to represent the retrieval results and embed the rank-prior of images to discriminate edge weight in the graph. Finally, the new relevance scores of images are deduced to re-rank images. Evaluation on two public datasets demonstrates the effectiveness of our approach.
Team possession analysis for broadcast soccer video based on ball trajectory

This paper presents a framework to automatically analyze the team possession for broadcast soccer video. Our framework requires the position of the ball and the play/break structure information as input. This problem is important in the cases of coaches and viewers evaluating team performance, and in the border context of video content understanding. The local minima of ball velocity curve over frames are a good start for finding possession transition points. The local minima and maxima in X and Y curves of the ball position, the ball and player locations, and acceleration starting points of the ball are used to infer the touching points, which are potential ball possession transition points. A support vector machine is employed to find out which team touches the ball for each touching point. Experimental results on two consecutive 1000-frame sequences illustrate that the proposed framework is promising.
Less is More: Efficient 3-D Object Retrieval With Query View Selection

The explosively increasing 3-D objects make their efficient retrieval technology highly desired. Extensive research efforts have been dedicated to view-based 3-D object retrieval for its advantage of using 2-D views to represent 3-D objects. In this paradigm, typically the retrieval is accomplished by matching the views of the query object with the objects in database. However, using all the query views may not only introduce difficulty in rapid retrieval but also degrade retrieval accuracy when there is a mismatch between the query views and the object views in the database. In this work, we propose an interactive 3-D object retrieval scheme. Given a set of query views, we first perform clustering to obtain several candidates. We then incrementally select query views for object matching: in each round of relevance feedback, we only add the query view that is judged to be the most informative one based on the labeling information. In addition, we also propose an efficient approach to learn a distance metric for the newly selected query view and the weights for combining all of the selected query views. We conduct experiments on the National Taiwan University 3D Model database, ETH 3D object collection, and Shape Retrieval Content of Non-Rigid 3D Model, and results demonstrated that our approach not only significantly speeds up the retrieval process but also achieves encouraging retrieval performance.
Coarse-to-Fine Description for Fine-Grained Visual Categorization

Recent years have witnessed the significant advance in fine-grained visual categorization, which targets to classify the objects belonging to the same species. To capture enough subtle visual differences and build discriminative visual description, most of the existing methods heavily rely on the artificial part annotations, which are expensive to collect in real applications. Motivated to conquer this issue, this paper proposes a multi-level coarse-to-fine object description. This novel description only requires the original image as input, but could automatically generate visual descriptions discriminative enough for fine-grained visual categorization. This description is extracted from five sources representing coarse-to-fine visual clues: 1) original image is used as the source of global visual clue; 2) object bounding boxes are generated using convolutional neural network (CNN); 3) with the generated bounding box, foreground is segmented using the proposed k nearest neighbour-based co-segmentation algorithm; and 4) two types of part segmentations are generated by dividing the foreground with an unsupervised part learning strategy. The final description is generated by feeding these sources into CNN models and concatenating their outputs. Experiments on two public benchmark data sets show the impressive performance of this coarse-to-fine description, i.e., classification accuracy achieves 82.5% on CUB-200-2011, and 86.9% on fine-grained visual categorization-Aircraft, respectively, which outperform many recent works.
On pixel count based crowd density estimation for visual surveillance

Surveillance systems for public security are going beyond the conventional CCTV. A new generation of systems relies on image processing and computer vision techniques, deliver more ready-to-use information, and provide assistance for early detection of unusual events. Crowd density is a useful source of information because unusual crowdedness is often related to unusual events. Previous works on crowd density estimation either ignore perspective distortion or perform the correction based on incorrect formulation. Also there is no investigation on whether the geometric correction derived for the ground plane can be applied to human objects standing upright to the plane. This paper derives the relation for geometric correction for the ground plane and proves formally that it can be directly applied to all the foreground pixels. We also propose a very efficient implementation because it is important for a real-time application. Finally a time-adaptive criterion for unusual crowdedness detection is described.
Retargeting Semantically-Rich Photos

Semantically-rich photos contain a rich variety of semantic objects (e.g., pedestrians and bicycles). Retargeting these photos is a challenging task since each semantic object has fixed geometric characteristics. Shrinking these objects simultaneously during retargeting is prone to distortion. In this paper, we propose to retarget semantically-rich photos by detecting photo semantics from image tags, which are predicted by a multi-label SVM. The key technique is a generative model termed latent stability discovery (LSD). It can robustly localize various semantic objects in a photo by making use of the predicted noisy image tags. Based on LSD, a feature fusion algorithm is proposed to detect salient regions at both the low-level and high-level. These salient regions are linked into a path sequentially to simulate human visual perception . Finally, we learn the prior distribution of such paths from aesthetically pleasing training photos. The prior enforces the path of a retargeted photo to be maximally similar to those from the training photos. In the experiment, we collect 217 1600 ×1200 photos, each containing over seven salient objects. Comprehensive user studies demonstrate the competitiveness of our method.
Geometric ℓp-norm feature pooling for image classification

Modern visual classification models generally include a feature pooling step, which aggregates local features over the region of interest into a statistic through a certain spatial pooling operation. Two commonly used operations are the average and max poolings. However, recent theoretical analysis has indicated that neither of these two pooling techniques may be qualified to be optimal. Besides, we further reveal in this work that more severe limitations of these two pooling methods are from the unrecoverable loss of the spatial information during the statistical summarization and the underlying over-simplified assumption about the feature distribution. We aim to address these inherent issues in this work and generalize previous pooling methods as follows. We define a weighted ℓp-norm spatial pooling function tailored for the class-specific feature spatial distribution. Moreover, a sensible prior for the feature spatial correlation is incorporated. Optimizing such pooling function towards optimal class separability yields a so-called geometric ℓp-norm pooling (GLP) method. The described GLP method is capable of preserving the class-specific spatial/geometric information in the pooled features and significantly boosts the discriminating capability of the resultant features for image classification. Comprehensive evaluations on several image benchmarks demonstrate that the proposed GLP method can boost the image classification performance with a single type of feature to outperform or be comparable with the state-of-the-arts.
Batch-Orthogonal Locality-Sensitive Hashing for Angular Similarity

Sign-random-projection locality-sensitive hashing (SRP-LSH) is a widely used hashing method, which provides an unbiased estimate of pairwise angular similarity, yet may suffer from its large estimation variance. We propose in this work batch-orthogonal locality-sensitive hashing (BOLSH), as a significant improvement of SRP-LSH. Instead of independent random projections, BOLSH makes use of batch-orthogonalized random projections, i.e, we divide random projection vectors into several batches and orthogonalize the vectors in each batch respectively. These batch-orthogonalized random projections partition the data space into regular regions, and thus provide a more accurate estimator. We prove theoretically that BOLSH still provides an unbiased estimate of pairwise angular similarity, with a smaller variance for any angle in (0, π), compared with SRP-LSH. Furthermore, we give a lower bound on the reduction of variance. The extensive experiments on real data well validate that with the same length of binary code, BOLSH may achieve significant mean squared error reduction in estimating pairwise angular similarity. Moreover, BOLSH shows the superiority in extensive approximate nearest neighbor (ANN) retrieval experiments.
Multimodal Gaussian Process Latent Variable Models with Harmonization

In this work, we address multimodal learning problem with Gaussian process latent variable models (GPLVMs) and their application to cross-modal retrieval. Existing GPLVM based studies generally impose individual priors over the model parameters and ignore the intrinsic relations among these parameters. Considering the strong complementarity between modalities, we propose a novel joint prior over the parameters for multimodal GPLVMs to propagate multimodal information in both kernel hyperparameter spaces and latent space. The joint prior is formulated as a harmonization constraint on the model parameters, which enforces the agreement among the modality-specific GP kernels and the similarity in the latent space. We incorporate the harmonization mechanism into the learning process of multimodal GPLVMs. The proposed methods are evaluated on three widely used multimodal datasets for cross-modal retrieval. Experimental results show that the harmonization mechanism is beneficial to the GPLVM algorithms for learning non-linear correlation among heterogeneous modalities.
Task-Driven Dynamic Fusion: Reducing Ambiguity in Video Description

Integrating complementary features from multiple channels is expected to solve the description ambiguity problem in video captioning, whereas inappropriate fusion strategies often harm rather than help the performance. Existing static fusion methods in video captioning such as concatenation and summation cannot attend to appropriate feature channels, thus fail to adaptively support the recognition of various kinds of visual entities such as actions and objects. This paper contributes to: 1)The first in-depth study of the weakness inherent in data-driven static fusion methods for video captioning. 2) The establishment of a task-driven dynamic fusion (TDDF) method. It can adaptively choose different fusion patterns according to model status. 3) The improvement of video captioning. Extensive experiments conducted on two well-known benchmarks demonstrate that our dynamic fusion method outperforms the state-of-the-art results on MSVD with METEOR scores 0.333, and achieves superior METEOR scores 0.278 on MSR-VTT-10K. Compared to single features, the relative improvement derived from our fusion method are 10.0% and 5.7% respectively on two datasets.
Interaction part mining: A mid-level approach for fine-grained action recognition

Modeling human-object interactions and manipulating motions lies in the heart of fine-grained action recognition. Previous methods heavily rely on explicit detection of the object being interacted, which requires intensive human labour on object annotation. To bypass this constraint and achieve better classification performance, in this work, we propose a novel fine-grained action recognition pipeline by interaction part proposal and discriminative mid-level part mining. Firstly, we generate a large number of candidate object regions using off-the-shelf object proposal tool, e.g., BING. Secondly, these object regions are matched and tracked across frames to form a large spatio-temporal graph based on the appearance matching and the dense motion trajectories through them. We then propose an efficient approximate graph segmentation algorithm to partition and filter the graph into consistent local dense sub-graphs. These sub-graphs, which are spatio-temporal sub-volumes, represent our candidate interaction parts. Finally, we mine discriminative mid-level part detectors from the features computed over the candidate interaction parts. Bag-of-detection scores based on a novel Max-N pooling scheme are computed as the action representation for a video sample. We conduct extensive experiments on human-object interaction datasets including MPII Cooking and MSR Daily Activity 3D. The experimental results demonstrate that the proposed framework achieves consistent improvements over the state-of-the-art action recognition accuracies on the benchmarks, without using any object annotation.
Discriminant Learning Through Multiple Principal Angles for Visual Recognition

Canonical correlation has been prevalent for multiset-based pairwise subspace analysis. As an extension, discriminant canonical correlations (DCCs) have been developed for classification purpose by learning a global subspace based on Fisher discriminant modeling of pairwise subspaces. However, the discriminative power of DCCs is not optimal as it only measures the “local” canonical correlations within subspace pairs, which lacks the “global” measurement among all the subspaces. In this paper, we propose a multiset discriminant canonical correlation method, i.e., multiple principal angle (MPA). It jointly considers both “local” and “global” canonical correlations by iteratively learning multiple subspaces (one for each set) as well as a global discriminative subspace, on which the angle among multiple subspaces of the same class is minimized while that of different classes is maximized. The proposed computational solution is guaranteed to be convergent with much faster converging speed than DCC. Extensive experiments on pattern recognition applications demonstrate the superior performance of MPA compared to existing subspace learning methods.
Multi parameter estimation of wideband Chirp signals based on OMP decomposition

Sparse decomposition—orthogonal matching pursuit is applied to array signal processing field, and a new method for multi parameters estimation of wideband Chirp signals based on orthogonal matching pursuit decomposition is presented. At first, according to wideband Chirp signal geometry, we establish the over-complete dictionary and do OMP decomposition to the array receive signal on the over-complete dictionary. According to the parameter of the best match atom, we get the estimation of starting frequency and modulating frequency slope of the signal, and then we get the wideband Chirp signal geometry. Based on this, according to the array signal structure and the wideband Chirp signal geometry which we have got, establish another over-complete dictionary. By calculating the trace of cross-correlation matrix between the array receive data and the atom in the atom dictionary, search the maximum peak value to get the best match atom. Then, according to the parameter of the best atom, get the direction of arrive estimation of the signal. Simulation results demonstrate that the algorithm is efficient to the parameter estimation. And compared to Wigner-Ville Distribution algorithm, this method can estimate the signal's direction of arrive more efficiently.
Use estimation of Performance Index to realize adaptive blind source separation

For adaptive blind source separation problem, by estimating the mixing matrix, then we can get the global transfer matrix, further we can obtain the estimation of PI (Performance Index), so we propose a new variable step-size blind adaptive algorithm based on EASI (Equivariant Adaptive Separation via Independence) algorithm. The accuracy of separation is determined by the global transfer matrix, that is to say, the step-size of EASI algorithm is adjusted by the accuracy of separation, which can be controlled by the estimation of PI. When the separation result with low precision, the step-size is biggish; on the contrary, accuracy increases, the step-size decreases. The simulation results show that the performance of the proposed algorithm is superior to the conventional EASI algorithm and has improved the separation result of the blind sources.