nils-herrmann/clean_scirepeval huggingface dataset:
Robust Sonar ATR with pose corrected sparse reconstruction-based classification

Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture Sonar (SAS). Because of this, sophisticated classification techniques originally developed for other tasks can be used in Sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC) which has shown an impressive resiliency to noise, blur, and occlusion even in settings with little training. We present a coherent strategy for using SRC for Sonar ATR that retains SRC's robustness while also being able to handle targets with diverse geometric arrangements. Our method, pose corrected sparsity (PCS), incorporates state-of-the-art dictionary learning schemes on localized block extractions which we show produces compelling classification results on the RAWSAS dataset.
Scalable robust hypothesis tests using graphical models

Traditional binary hypothesis testing relies on the precise knowledge of the probability density of an observed random vector conditioned on each hypothesis. However, for many applications, these densities can only be approximated due to limited training data or dynamic changes affecting the observed signal. A classical approach to handle such scenarios of imprecise knowledge is via minimax robust hypothesis testing (RHT), where a test is designed to minimize the worst case performance for all models in the vicinity of the approximated imprecise density. Despite the promise of RHT for robust classification problems, its applications have remained rather limited because RHT in its native form does not scale gracefully with the dimension of the observed random vector. In this paper, we use approximations via probabilistic graphical models, in particular block-tree graphs, to enable computationally tractable algorithms for realizing RHT on high-dimensional data. We quantify the reductions in computational complexity. Experimental results on simulated data and a target recognition problem show minimal loss over a true RHT.
Multi-task image classification via collaborative, hierarchical spike-and-slab priors

Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of class-specific spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.
Localized dictionary design for geometrically robust sonar ATR

Advancements in Sonar image capture have opened the door to powerful classification schemes for automatic target recognition (ATR). Recent work has particularly seen the application of sparse reconstruction-based classification (SRC) to sonar ATR, which provides compelling accuracy rates even in the presence of noise and blur. However, existing sparsity based sonar ATR techniques assume that the test images exhibit geometric pose that is consistent with respect to the training set. This work addresses the outstanding open challenge of handling inconsistently posed Sonar images relative to training. We develop a new localized block-based dictionary design that can enable geometric robustness. Further, a dictionary learning method is incorporated to increase performance and efficiency. The proposed SRC with Localized Pose Management (LPM), is shown to outperform the state of the art SIFT feature and SVM approach, due to its power to discern background clutter in Sonar images.
A map estimation framework for HDR video synthesis

High dynamic range (HDR) image synthesis from multiple low dynamic range (LDR) exposures continues to be a topic of great interest. The extension to HDR video comprises a stiff challenge due to significant motion. In particular, loss of data due to poor exposures introduces great difficulty in exact motion estimation, and under such circumstances conventional optical flow calculation techniques usually fail. We propose a maximum a posterior (MAP) estimation framework for HDR video synthesis algorithm free of explicit optical flow calculation. We formulate HDR video synthesis as a MAP estimation problem, which subsequently can be reduced to an optimization problem based on meaningful statistical assumptions on foreground and background regions of the input video. In the background regions the underlying scenes are static, while in the foreground regions motion information is captured implicitly by a modified 3D steering kernel regression (3D SKR) approach. Solution to the optimization problem provides us with temporally coherent HDR video sequences without noticeable artifacts. Experimental results on challenging LDR video sets demonstrate that our proposed algorithm can achieve HDR video quality that is competitive with or better than state of the art alternatives.
Tensor sparsity for classifying low-frequency ultra-wideband (UWB) SAR imagery

Although a lot of progress has been made over the years, one critical challenge still facing low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology is the discrimination of buried and obscured targets of interest from other natural and manmade clutter objects in the scene. The key issues are i) low-resolution SAR imagery for this frequency band, ii) targets of interests being typically small compared to the radar signal wavelengths, iii) targets having low radar cross sections (RCS), and iv) very noisy SAR imagery (e.g. target responses buried in responses from cluttered environment). In this paper, we consider the problem of discriminating and classifying buried targets of interest (buried metal and plastic mines, 155-mm unexploded ordinance [UXO], etc.) from other natural and manmade clutter objects (soda can, rocks, etc.) in the presence of noisy responses from the rough ground surfaces for low-frequency UWB 2-D SAR images. We generalize the traditional sparse representation-based classification (SRC) to a model with capability of using the information of the shared class, and implement multichannel classification problems by exploiting structures of sparse coefficients using various techniques. Here, we employ an electromagnetic (EM) SAR database generated using the finite-difference, time-domain (FDTD) software, which is based on a full-wave computational EM method.
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, the Laplacian being the most common such choice (leading to l1-norm minimization). The recent seminal contribution by Wright et al. exploits the discriminative capability of sparse representations for image classification, specifically face recognition. Their approach employs the analytical framework of CS with class-specific dictionaries. Our contribution is a logical extension of these ideas into structured sparsity for classification. We use class-specific dictionaries in conjunction with discriminative class-specific priors, specifically the spike-and-slab prior widely applied in Bayesian 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.
Practical methods for sparsity based 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. Recently, sparse reconstruction techniques have been used for image classification, and shown to provide excellent robustness to occlusion. This progress has also been leveraged for sparsity based video anomaly detection where test trajectories are expressed as sparse linear combinations of example trajectories from a given (normal or anomalous) class. While sparsity based anomaly detection techniques are promising, they pose practical challenges due to their increased computational burden and the need for generous manually labeled training (even if only for normal event trajectories). Our work focuses on overcoming these limitations. Our central contribution is a dictionary design and optimization technique that can effectively reduce the size of training dictionaries that enable sparsity based classification/anomaly detection without adversely influencing detection performance. We also suggest the use of state of the art automatic trajectory clustering techniques for initializing dictionaries which can alleviate the burden of manual labeling. Experimental results show that significant computational advantages can be obtained with the proposed techniques with little performance loss over using large and manually labeled dictionaries of example trajectories.
Estimation of structured covariance matrices for radar STAP under practical constraints

Estimation of the disturbance or interference covariance matrix plays a central role in radar target detection. Traditional maximum likelihood (ML) estimators lead to degraded false alarm and detection performance in the realistic regime of limited training. For this reason, structured covariance estimators have been actively researched. This paper reviews as well as proposes new structured covariance estimation methods which exploit physically motivated practical constraints. We first review the rank constrained maximum likelihood (RCML) estimator which explicitly incorporates the rank of the clutter subspace as a constraint in the ML problem. Next, we introduce an efficient approximation of structured covariance under joint Toeplitz and rank constraint (EASTR). In particular, we propose new quadratic optimization problems that enforce Toeplitz structure while preserving rank. Crucially, both the RCML estimator and the EASTR admit closed form solutions and hence facilitate real time implementation. We perform experimental evaluation in the form of normalized SINR, probability of detection, and whiteness tests. In each case, we compare against widely used existing estimators and show that exploiting the practical constraints has significant merits in covariance estimation.
Rank constrained ML estimation of structured covariance matrices with applications in radar target detection

We consider here the continually important problem of radar target detection in the presence of clutter, noise and jamming. Under complex Gaussian noise statistics, the optimal detection statistic relies on an inversion of the disturbance (clutter + noise and jamming) covariance matrix. The disturbance (and hence clutter) covariance must be estimated in practice from sample, i.e. training observations. Traditional maximum likelihood (ML) estimators are effective when training is abundant but lead to poor estimates and hence high detection error in the realistic regime of limited or small training. The problem is exacerbated by recent advances which have led to high dimensionality N of the observations arising from increased antenna elements (J) as well as higher temporal resolution (P time epochs and finally N = J.P). This work introduces physically inspired constraints into ML estimation. In particular, we exploit both the structure of the disturbance covariance and importantly the knowledge of the clutter rank to yield a new rank constrained maximum likelihood (RCML) estimator of clutter/disturbance covariance. Experimental validation on the KASSPER data set (where ground truth covariance is made available) shows that the proposed estimator vastly outperforms state-of-the art alternatives in the sense of: 1.) higher normalized signal to interference and noise ratio (SINR), and 2.) lower variance of target amplitude estimators that utilize disturbance covariance. Crucially the proposed RCML estimator can excel even for low training including the notoriously difficult regime of K ≤ N training samples.
A constrained optimization perspective on joint spatial resolution and dynamic range enhancement

The problem of resolution enhancement in images from multiple low-resolution captures has garnered significant attention over the last decade. While initial algorithms estimated the unknown high-resolution (hi-res) image for a fixed set of imaging model parameters, significant recent advances have been in simultaneous maximum aposteriori (MAP) estimation of the hi-res image as well as the geometric registration parameters under a variety of noise and prior models. A key computational challenge however, lies in the algorithmic tractability of the resulting optimization problem. Independently, there has been a surge in approaches for enhancing amplitude (or dynamic range) resolution in images from multiple captures. We develop a novel constrained optimization framework to address the problem of joint estimation of imaging model parameters and the unknown hi-res, high dynamic range image. In this framework, we employ a transformation of variables to establish separable convexity of the cost function under any lp norm, p ≥ 1, in the individual variables of geometric and photometric registration parameters, optical blur and the unknown hi-res image. We formulate evolving convex constraints which ensure that the registration parameters as well as the reconstructed image remain physically meaningful. The convergence guarantee afforded by our algorithm alleviates unreasonable demands on initialization, and produces reconstructed image results approaching practical upper bounds. Several existing formulations reduce to special cases of our framework making the algorithm broadly applicable.
Robust Video Hashing via Multilinear Subspace Projections

The goal of video hashing is to design hash functions that summarize videos by short fingerprints or hashes. While traditional applications of video hashing lie in database searches and content authentication, the emergence of websites such as YouTube and DailyMotion poses a challenging problem of anti-piracy video search. That is, hashes or fingerprints of an original video (provided to YouTube by the content owner) must be matched against those uploaded to YouTube by users to identify instances of “illegal” or undesirable uploads. Because the uploaded videos invariably differ from the original in their digital representation (owing to incidental or malicious distortions), robust video hashes are desired. We model videos as order-3 tensors and use multilinear subspace projections, such as a reduced rank parallel factor analysis (PARAFAC) to construct video hashes. We observe that, unlike most standard descriptors of video content, tensor-based subspace projections can offer excellent robustness while effectively capturing the spatio-temporal essence of the video for discriminability. We introduce randomization in the hash function by dividing the video into (secret key based) pseudo-randomly selected overlapping sub-cubes to prevent against intentional guessing and forgery. Detection theoretic analysis of the proposed hash-based video identification is presented, where we derive analytical approximations for error probabilities. Remarkably, these theoretic error estimates closely mimic empirically observed error probability for our hash algorithm. Furthermore, experimental receiver operating characteristic (ROC) curves reveal that the proposed tensor-based video hash exhibits enhanced robustness against both spatial and temporal video distortions over state-of-the-art video hashing techniques.
Simultaneous sparsity model for multi-perspective 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 has shown promise over alternate video anomaly detection methods in that the sparse representations exhibit excellent robustness under noise (common in surveillance videos) and missing or corrupted features, e.g. vehicle occlusion in transportation videos. One limitation of existing sparsity based video anomaly detection techniques is that they are based on only a single feature representation (known formally as video event encoding). One can easily envision that different event representations such as object trajectories and spatio-temporal volumes often contain correlated yet complementary information. In this paper, we propose to extend sparsity models based on single feature representations to simultaneous sparse representations of multiple feature representations. In this model, the matrix of sparse coefficients does not confirm to the commonly seen row-sparsity and a modified greedy heuristic approach that extends simultaneous orthogonal matching pursuit (SOMP) is needed to solve the resulting optimization problem. Experiments on two benchmark video datasets reveal that our method significantly outperforms state-of-the art approaches that utilize only a single-perspective or event encoding.
Discriminative sparse representations with applications

Significant advances in compressive sensing and sparse signal encoding have provided a rich set of mathematical tools for signal analysis and representation. In addition to novel formulations for enabling sparse solutions to underdetermined systems, exciting progress has taken place in efficiently solving these problems from an optimization theoretic viewpoint. The focus of the wide body of literature in compressive sensing/sparse signal representations has however been on the problem of signal recovery from a small number of measurements (equivalently a sparse coefficient vector). This tutorial will discuss the design of sparse signal representations explicitly for the purposes of signal classification. The tutorial will focus on and build upon two significant recent advances. First, the work by Wright et al. which advocates the use of a dictionary (or basis) matrix comprising of class-specific training sub-dictionaries. In this framework, a test signal is modeled as a sparse linear combination of training vectors in the dictionary, sparsity being enforced by the assertion that only coefficients corresponding to one class (from which the test signal is drawn) ought to be active. The second set of ideas we leverage are recent key contributions in model-based compressive sensing where prior information or constraints on sparse coefficients are used to enhance signal recovery. These ideas will be combined towards the exposition of current trends: namely the development of class-specific priors or constraints to capture structure on sparse coefficients that helps explicitly distinguish between signal classes. In the second part of the tutorial, applications will be discussed including: 1.) structured sparsity for classification of medical imagery for diagnostics, 2.) low-rank approximation and sparse recovery for visual data reconstruction, and 3.) sparse representations for target detection and classification in hyperspectral imagery (guest speaker from the US Army Research Lab).
SAR Automatic Target Recognition Using Discriminative Graphical Models

The problem of automatically classifying sensed imagery such as synthetic aperture radar (SAR) into a canonical set of target classes is widely known as automatic target recognition (ATR). A typical ATR algorithm comprises the extraction of a meaningful set of features from target imagery followed by a decision engine that performs class assignment. While ATR algorithms have significantly increased in sophistication over the past two decades, two outstanding challenges have been identified in the rich body of ATR literature: 1) the desire to mine complementary merits of distinct feature sets (also known as feature fusion), and 2) the ability of the classifier to excel even as training SAR images are limited. We propose to apply recent advances in probabilistic graphical models to address these challenges. In particular we develop a two-stage target recognition framework that combines the merits of distinct SAR image feature representations with discriminatively learned graphical models. The first stage projects the SAR image chip to informative feature spaces that yield multiple complementary SAR image representations. The second stage models each individual representation using graphs and combines these initially disjoint and simple graphs into a thicker probabilistic graphical model by leveraging a recent advance in discriminative graph learning. Experimental results on the benchmark moving and stationary target acquisition and recognition (MSTAR) data set confirm the benefits of our framework over existing ATR algorithms in terms of improvement in recognition rates. The proposed graphical classifiers are particularly robust when feature dimensionality is high and number of training images is small, a commonly observed constraint in SAR imagery-based target recognition.
Optimally robust extrema filters for time series data

Feature vectors encoded by using extrema are known to be immune to different types of distortions of the original time series . This property enables them to be effective in a wide range of pattern matching applications for time series data  . The process of extracting extrema is usually preceded by a filtering step to reduce noise and to bring out prominent features in a time series. The core contribution of this paper is a methodology based on eigenanalysis to optimize the filter that would lead to robust extrema being extracted from the filtered signal. In this context, robustness is understood as the ability of the extrema from a signal to remain intact in spite of distortions to the signal. The paper then demonstrates that the `optimally robust' extrema outperform extrema obtained from using traditional filters in a time series pattern matching (subsequence matching) task on real and simulated datasets in the presence of bias, scale factor, and outlier distortions in the query signal.
Adaptive matching pursuit for sparse signal recovery

Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification. It is well-known that solving for the sparse coefficient vector to maximize these priors results in a hard non-convex and mixed integer programming problem. Most existing solutions to this optimization problem either involve simplifying assumptions/relaxations or are computationally expensive. We propose a new greedy and adaptive matching pursuit (AMP) algorithm to directly solve this hard problem. Essentially, in each step of the algorithm, the set of active elements would be updated by either adding or removing one index, whichever results in better improvement. In addition, the intermediate steps of the algorithm are calculated via an inexpensive Cholesky decomposition which makes the algorithm much faster. Results on simulated data sets as well as real-world image recovery challenges confirm the benefits of the proposed AMP, particularly in providing a superior cost-quality trade-off over existing alternatives.