nils-herrmann/clean_scirepeval huggingface dataset:
Low rank sparsity prior for robust video anomaly detection

Recently, sparsity based classification has been applied to video anomaly detection. A linear model is assumed over video features (e.g. trajectories) such that the feature representation of a new event is written as a sparse linear combination of existing feature representations in the dictionary. Sparsity based video anomaly detection shows promise but open challenges remain in that existing methods assume object specific and class specific event dictionaries making them applicable mostly in highly structured scenarios. Second, using conventional sparsity models on matrices/vectors, the computational burden is often high. In this work, we advocate a more general and practical sparsity model using a low-rank structure on the matrix of sparse coefficients. We find that enforcing a low-rank structure can ease the rigidity of traditional row-sparse constraints on sparse coefficient vectors/matrices. Because low-rank matrices are of course not always sparse, an additional l1 regularization term is added. Further, if rank is substituted by its convex nuclear norm alternative, then significant computational benefits can be obtained over existing methods in sparsity based video anomaly detection. Experimental evaluation on benchmark video datasets reveal, our method is competitive with state-of-the art while providing robustness benefits under occlusion.
Structured Sparse Priors for Image Classification

Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, with the Laplacian being the most common such choice (leading to l1-norm minimization). Recent work has exploited the discriminative capability of sparse representations for image classification by employing class-specific dictionaries in the CS framework. Our contribution is a logical extension of these ideas into structured sparsity for classification. We introduce the notion of discriminative class-specific priors in conjunction with class specific dictionaries, specifically the spike-and-slab prior widely applied in Bayesian sparse regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes. We demonstrate this practical benefit of our approach in important applications, such as face recognition and object categorization.
Simultaneous Sparsity Model for Histopathological Image Representation and Classification

The multi-channel nature of digital histopathological images presents an opportunity to exploit the correlated color channel information for better image modeling. Inspired by recent work in sparsity for single channel image classification, we propose a new simultaneous sparsity model for multi-channel histopathological image representation and classification (SHIRC). Essentially, we represent a histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints. Classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. A practical challenge is the correspondence of image objects (cellular and nuclear structures) at different spatial locations in the image. We propose a robust locally adaptive variant of SHIRC (LA-SHIRC) to tackle this issue. Experiments on two challenging real-world image data sets: 1) mammalian tissue images acquired by pathologists of the animal diagnostics lab (ADL) at Pennsylvania State University, and 2) human intraductal breast lesions, reveal the merits of our proposal over state-of-the-art alternatives. Further, we demonstrate that LA-SHIRC exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training per class is often not available.
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.
Successive QCQP Refinement for MIMO Radar Waveform Design Under Practical Constraints

The authors address the problem of designing a waveform for multiple-input multiple-output (MIMO) radar under the important practical constraints of constant modulus and waveform similarity. Incorporating these constraints in an analytically tractable manner is a longstanding open challenge. This is due to the fact that the optimization problem that results from signal-to-interference-plus-noise ratio (SINR) maximization subject to these constraints is a hard non-convex problem. The authors develop a new analytical approach that involves solving a sequence of convex quadratically constrained quadratic programing (QCQP) problems, which they prove converges to a sub-optimal solution. Because an improvement in SINR results via solving each problem in the sequence, they call the method Successive QCQP Refinement (SQR). Furthermore, the proposed SQR method can be easily extended to incorporate emerging requirements of spectral coexistence, as shown briefly in this paper. The authors evaluate SQR against other candidate techniques with respect to SINR performance, beam pattern, and pulse compression properties in a variety of scenarios. Results show that SQR outperforms state-of-the-art methods that also employ constant modulus and/or similarity constraints while being computationally less burdensome.
Adaptive Sparse Representations for Video Anomaly Detection

Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. A common class of approaches relies on object tracking and trajectory analysis. Very recently, sparse reconstruction techniques have been employed in video anomaly detection. The fundamental underlying assumption of these methods is that any new feature representation of a normal/anomalous event can be approximately modeled as a (sparse) linear combination prelabeled feature representations (of previously observed events) in a training dictionary. Sparsity can be a powerful prior on model coefficients but challenges remain in the detection of anomalies involving multiple objects and the ability of the linear sparsity model to effectively allow for class separation. The proposed research addresses both these issues. First, we develop a new joint sparsity model for anomaly detection that enables the detection of joint anomalies involving multiple objects. This extension is highly nontrivial since it leads to a new simultaneous sparsity problem that we solve using a greedy pursuit technique. Second, we introduce nonlinearity into, that is, kernelize. The linear sparsity model to enable superior class separability and hence anomaly detection. We extensively test on several real world video datasets involving both single and multiple object anomalies. Results show marked improvements in detection of anomalies in both supervised and unsupervised scenarios when using the proposed sparsity models.
Twofold Video Hashing With Automatic Synchronization

As a robust media representation technique, video hashing is frequently used in near-duplicate detection, video authentication, and antipiracy search. Distortions to a video may include spatial modifications to each frame, temporal de-synchronization, and joint spatio-temporal attacks. To address the increasingly difficult case of finding videos under spatio-temporal modifications, we propose a new framework called two-stage video hashing. First, an efficient automatic synchronization is achieved using dynamic time warping (DTW) and a complementary video comparison measure is developed based on flow hashing (FH), which is extracted from the synchronized videos. Next, a fusion mechanism called distance boosting is proposed to fuse the information extracted by DTW and FH in a future-proof manner in the sense whenever model retraining is needed, the existing hash vectors do not need to be regenerated. Experiments on real video collections show that such a hash extraction and fusion method enables unprecedented robustness under both spatial and temporal attacks.
ICR: Iterative Convex Refinement for Sparse Signal Recovery Using Spike and Slab Priors

In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, and existing solutions involve simplifying assumptions and/or relaxations. We propose an approach called Iterative Convex Refinement (ICR) that aims to solve the aforementioned optimization problem directly allowing for greater generality in the sparse structure. Essentially, ICR solves a sequence of convex optimization problems such that sequence of solutions converges to a sub-optimal solution of the original hard optimization problem. We propose two versions of our algorithm: a.) an unconstrained version, and b.) with a non-negativity constraint on sparse coefficients, which may be required in some real-world problems. Experimental validation is performed on both synthetic data and for a real-world image recovery problem, which illustrates merits of ICR over state of the art alternatives.
Fast stochastic hierarchical Bayesian map for tomographic imaging

Any image recovery algorithm attempts to achieve the highest quality reconstruction in a timely manner. The former can be achieved in several ways, among which are by incorporating Bayesian priors that exploit natural image tendencies to cue in on relevant phenomena. The Hierarchical Bayesian MAP (HB-MAP) is one such approach  which is known to produce compelling results albeit at a substantial computational cost. We look to provide further analysis and insights into what makes the HB-MAP work. While retaining the proficient nature of HB-MAP's Type-I estimation, we propose a stochastic approximation-based approach to Type-II estimation. The resulting algorithm, fast stochastic HB-MAP (fsHBMAP), takes dramatically fewer operations while retaining high reconstruction quality. We employ our fsHBMAP scheme towards the problem of tomographic imaging and demonstrate that fsHBMAP furnishes promising results when compared to many competing methods.
Constrained ML estimation of structured covariance matrices with applications in radar STAP

The disturbance covariance matrix in radar space time adaptive processing (STAP) must be estimated from training sample observations. Traditional maximum likelihood (ML) estimators are effective when training is generous but lead to degraded false alarm rates and detection performance in the realistic regime of limited training. We exploit physically motivated constraints such as 1.) rank of the clutter subspace which can be inferred using existing physics based models such as the Brennan rule, and 2.) the Toeplitz constraint that applies to covariance matrices obtained from stationary random processes. We first provide a closed form solution of the rank constrained maximum likelihood (RCML) estimator and then subsequently develop an efficient approximation under joint Toeplitz and rank constraints (EASTR). Experimental results confirm that the proposed EASTR estimators outperform state-of-the-art alternatives in the sense of widely used measures such as the signal to interference and noise ratio (SINR) and probability of detection - particularly when training support is limited.
Transmit MIMO beampattern design under constant modulus and spectral Interference Constraints

In multiple array radar systems, waveform diversity can be exploited to enhance the beampattern design. However, in a spectrally crowded environment, the spectral shape of radar waveforms must be compatible with civilian and other wireless communication services. We address the problem of designing a beampattern for MIMO radar under a spectral interference constraint. The problem essentially reduces to a constrained design of the radar waveform, where a key open challenge is jointly enforcing the spectral interference constraint in addition to the constant modulus constraint (CMC). A new approach is proposed in our work, which involves solving a sequence of convex Constrained Quadratic Programs, each of which has a closed form solution and such that constant modulus is achieved at convergence. New analytical results are provided in support of the proposed Beampattern design under constant modulus and Interference Constraints (BIC). We evaluate the proposed BIC algorithm against state of the art MIMO beampattern design methods that enforce constant modulus and show that BIC achieves a high fidelity radar beampattern while maintaining low spectral interference in the desired bands.
Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.
Efficient approximation of structured covariance under joint Toeplitz and rank constraints

The disturbance (clutter plus noise and jamming) covariance matrix which plays a central role in radar space time adaptive processing (STAP) should be estimated from sample training observations in practice. Traditional maximum likelihood (ML) estimators lead to degraded false alarm and detection performance in the realistic regime of limited training. Therefore constrained ML estimation has received much attention which exploits structure and other properties that a disturbance covariance matrix exhibits. In this paper1, we derive a new covariance estimator for STAP that jointly considers a Toeplitz structure and a rank constraint on the clutter component. Past work has shown that in the regime of low training, even handling each constraint individually is hard and techniques often resort to slow numerically based solutions. Our proposed solution leverages a recent advance called rank constrained ML estimator (RCML) of structured covariances to build a computationally friendly approximation that involves a cascade of two closed form solutions. Experimental investigation shows that the proposed estimator outperforms state of the art alternatives in the sense of: normalized signal to interference and noise ratio (SINR), and probability of detection versus signal to noise ratio (SNR).
Algorithms for color look-up-table (LUT) design via joint optimization of node locations and output values

Real-time processing constraints entail that non-linear color transforms be implemented using multi-dimensional look-up-tables (LUT). Further, relatively sparse LUTs (with efficient interpolation) are employed in practice because of storage and memory constraints. Much research has been devoted towards optimizing “nodes” (or equivalently partitioning the input color space) of this color LUT based on the curvature of the color transform to be processed through the LUT. Likewise, for a given LUT structure, the optimization of transform output values has been suggested so as to minimize interpolation error in an expected sense even if the values stored in the LUT do not agree with true transform output values. This paper presents a principled algorithmic approach to combine the merits of these two complementary techniques. The error (cost) function does not exhibit joint convexity over the multidimensional variable sets of node locations and corresponding output values which makes this optimization particularly challenging. The paper makes two significant contributions: 1.) for the case of simplex interpolation, a cost function is formulated that exhibits separable convexity in its arguments and enables an efficient alternating convex optimization algorithm, and 2.) in the aforementioned framework, for fixed node outputs, the optimization of node locations is split into a primary and an auxiliary optimization, which greatly improves the quality of the solution over traditional alternatives where node locations are directly optimized. Preliminary experiments show remarkable improvements in color transform accuracy over what is obtained by individually optimizing just the node locations or output values.
Power-constrained RGB-to-RGBW conversion for emissive displays

We propose a novel power-constrained RGB-to-RGBW conversion algorithm for emissive RGBW displays. We measure the perceived color distortion using a color difference model in a perceptually uniform color space, and compute the power consumption for displaying an RGBW pixel on an emissive display. The main contribution is to formulate the optimization problem to minimize the color distortion subject to the constraint on the power consumption. Then, we solve it efficiently to convert an image in real time. Simulation results show that the proposed algorithm provides significantly less color distortion than the conventional methods while providing a graceful trade-off with the amount of power consumed.
Sparsity based super resolution using color channel constraints

Sparsity constrained single image super-resolution has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then use the coefficients of this representation to generate the high-resolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for super resolution only use luminance channel information and do not use any information from other color channels. In this work, we extend sparsity based super-resolution to multiple color channels. Edge similarities amongst color bands are exploited as cross channel correlation constraints. These additional constraints lead to a new optimization problem which is not easily solvable; however, a tractable solution is proposed to solve it efficiently. Experimental results shows the merits of our proposed method both visually and quantitatively.
Design and Optimization of Color Lookup Tables on a Simplex Topology

An important computational problem in color imaging is the design of color transforms that map color between devices or from a device-dependent space (e.g., RGB/CMYK) to a device-independent space (e.g., CIELAB) and vice versa. Real-time processing constraints entail that such nonlinear color transforms be implemented using multidimensional lookup tables (LUTs). Furthermore, relatively sparse LUTs (with efficient interpolation) are employed in practice because of storage and memory constraints. This paper presents a principled design methodology rooted in constrained convex optimization to design color LUTs on a simplex topology. The use of n simplexes, i.e., simplexes in n dimensions, as opposed to traditional lattices, recently has been of great interest in color LUT design for simplex topologies that allow both more analytically tractable formulations and greater efficiency in the LUT. In this framework of n-simplex interpolation, our central contribution is to develop an elegant iterative algorithm that jointly optimizes the placement of nodes of the color LUT and the output values at those nodes to minimize interpolation error in an expected sense. This is in contrast to existing work, which exclusively designs either node locations or the output values. We also develop new analytical results for the problem of node location optimization, which reduces to constrained optimization of a large but sparse interpolation matrix in our framework. We evaluate our n -simplex color LUTs against the state-of-the-art lattice (e.g., International Color Consortium profiles) and simplex-based techniques for approximating two representative multidimensional color transforms that characterize a CMYK xerographic printer and an RGB scanner, respectively. The results show that color LUTs designed on simplexes offer very significant benefits over traditional lattice-based alternatives in improving color transform accuracy even with a much smaller number of nodes.
SHIRC: A simultaneous sparsity model for histopathological image representation and classification

Automated classification of histopathological images is an important research problem in medical imaging. Digital histopathology exhibits two principally distinct characteristics: 1) invariably histopathological images are multi-channel (color) with key geometric information spread across the color channels instead of being captured by luminance alone, and 2) the richness of geometric structures in such tissue imagery makes feature extraction for classification very demanding. Inspired by recent work in the use of sparsity for single channel image classification, we propose a new simultaneous Sparsity model for multi-channel Histopathological Image Representation and Classification (SHIRC). Essentially, we represent a multi-channel histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints and classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. Experiments on two challenging real-world image databases: 1) provided by pathologists of the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 2) histopathological images corresponding to intraductal breast lesions , reveal the merits of the proposed SHIRC model over state of the art alternatives.