nils-herrmann/clean_scirepeval huggingface dataset:
A learning framework for robust hashing of face images

Robust image hashing has been actively researched over the last decade with varied applications in image content authentication and identification under distortions. In the existing literature on robust image hashing, hash algorithms are ignorant of the class of images being hashed. There are however significant application domains such as that of face image hashing where apriori knowledge of the image class as well as permissible distortions can benefit hash algorithm design. In this paper, we present a two stage cascade of dimensionality reduction constructs for face image hashing. The first stage aims to project the face image to a space where geometric distortions manifest approximately as additive noise. For this purpose, we use the non-negative matrix approximations based hash vector developed by Monga et al. which is known to possess excellent geometric attack robustness. In the second stage, we employ oriented principal component analysis (OPCA) based on estimating signal as well as noise statistics in a learning phase and deriving a projection that mitigates the effect of noise. We obtain both experimentally based ROC curves as well as analytical ones via a detection theoretic analysis of the proposed framework. The ROC curves reveal clearly that incorporating such a learning phase greatly reduces error probabilities.
Rank-Constrained Maximum Likelihood Estimation of Structured Covariance Matrices

This paper develops and analyzes the performance of a structured covariance matrix estimate for the important practical problem of radar space-time adaptive processing in the face of severely limited training data. Traditional maximum likelihood (ML) estimators are effective when training data are abundant, but they lead to poor estimates, degraded false alarm rates, and detection loss in the realistic regime of limited training. The problem is exacerbated by recent advances, which have led to high-dimensional N of the observations arising from increased antenna elements, as well as higher temporal resolution (P time epochs and finally N = JP). This work addresses the problem by incorporating constraints in the ML estimation problem obtained from the geometry and physics of the airborne phased array radar scenario. In particular, we exploit the structure of the disturbance covariance and, importantly, knowledge of the clutter rank to derive a new rank-constrained maximum likelihood (RCML) estimator of clutter and disturbance covariance. We demonstrate that despite the presence of the challenging rank constraint, the estimation can be transformed to a convex problem and derive closed-form expressions for the estimated covariance matrix. Performance analysis using the knowledge-aided sensor signal processing and expert reasoning data set (where ground truth covariance is made available) shows that the proposed estimator outperforms state-of-the-art alternatives in the sense of a higher normalized signal-to-interference and noise ratio. Crucially, the RCML estimator excels for low training, including the notoriously difficult regime of K ≤ N training samples.
Meta-classifiers for multimodal document classification

This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.
Discriminative sparsity for Sonar ATR

Advancements in Sonar image capture have enabled researchers to apply sophisticated object identification algorithms in order to locate targets of interest in images such as mines. Despite progress in this field, modern sonar automatic target recognition (ATR) approaches lack robustness to the amount of noise one would expect in real-world scenarios, the capability to handle blurring incurred from the physics of image capture, and the ability to excel with relatively few training samples. We address these challenges by adapting modern sparsity-based techniques with dictionaries comprising of training from each class. We develop new discriminative (as opposed to generative) sparse representations which can help automatically classify targets in Sonar imaging. Using a simulated SAS data set from the Naval Surface Warfare Center (NSWC), we obtained compelling classification rates for multi-class problems even in cases with considerable noise and sparsity in training samples.
Deep network for simultaneous decomposition and classification in UWB-SAR imagery

Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology. This technology has been used in various applications, including ground penetration and sensing-through-the-wall. However, the technology still faces a significant issue regarding low-resolution SAR imagery in this particular frequency band, low radar cross sections (RCS), small objects compared to radar signal wavelengths, and heavy interference. The classification problem has been firstly, and partially, addressed by sparse representation-based classification (SRC) method which can extract noise from signals and exploit the cross-channel information. Despite providing potential results, SRC-related methods have drawbacks in representing nonlinear relations and dealing with larger training sets. In this paper, we propose a Simultaneous Decomposition and Classification Network (SDCN) to alleviate noise inferences and enhance classification accuracy. The network contains two jointly trained sub-networks: the decomposition sub-network handles denoising, while the classification sub-network discriminates targets from confusers. Experimental results show significant improvements over a network without decomposition and SRC-related methods.
Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans

Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed.   Our contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images.  Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential.
Robust video fingerprinting via structural graphical models

Applications of video fingerprinting range from traditional video retrieval and authentication to the more recent problem of anti-piracy search brought about by the emergence of video websites such as Youtube. Video fingerprints offer the potential of identifying in a robust and scalable manner - illegal or undesirable uploads of copyrighted video content. The principal challenge in video fingerprinting is to extract reduced dimensionality descriptors that can withstand incidental spatial and temporal distortions to the video while still allowing the discrimination of distinct videos. To address this fundamental problem, we propose to first represent a video as a graphical structure which can encode temporal relationships between video shots that are crucial to uniquely identifying the video. Next, we leverage ideas from graph theory, namely the normalized cuts graph partitioning method to divide the video representation into sub-graphs. Robust dimensionality reduction applied to these sub-graphs yields the final video hash/fingerprint. Experimental results in the form of receiver operating characteristic (ROC) curves on video databases acquired from YouTube reveal that the proposed video fingerprinting can enable a much more favorable robustness vs. discriminability trade-off over state-of-the art algorithms in video hashing.
SIASM: Sparsity-based image alignment and stitching method for robust image mosaicking

Image alignment and stitching continue to be the topics of great interest. Image mosaicking is a key application that involves both alignment and stitching of multiple images. Despite significant previous effort, existing methods have limited robustness in dealing with occlusions and local object motion in different captures. To address this issue, we investigate the potential of applying sparsity-based methods to the task of image alignment and stitching. We formulate the alignment problem as a low-rank and sparse matrix decomposition problem under incomplete observations (multiple parts of a scene), and the stitching problem as a multiple labeling problem which utilizes the sparse components. Additionally we develop efficient algorithms for solving them. Unlike typical pairwise alignment manners in classical image alignment algorithms, our algorithm is capable of simultaneously aligning multiple images, making full use of inter-frame relationships among all images. Experimental results demonstrate that the proposed algorithm is capable of generating artifact-free stitched image mosaics that are robust against occlusions and object motion.
Analysis-synthesis model learning with shared features: A new framework for histopathological image classification

Automated histopathological image analysis offers exciting opportunities for the early diagnosis of several medical conditions including cancer. There are however stiff practical challenges: 1.) discriminative features from such images for separating diseased vs. healthy classes are not readily apparent, and 2.) distinct classes, e.g. healthy vs. stages of disease continue to share several geometric features. We propose a novel Analysis-synthesis model Learning with Shared Features algorithm (ALSF) for classifying such images more effectively. In ALSF, a joint analysis and synthesis learning model is introduced to learn the classifier and the feature extractor at the same time. In this way, the computation load in patch-level based image classification can be much reduced. Crucially, we integrate into this framework the learning of a low rank shared dictionary and a shared analysis operator, which more accurately represents both similarities and differences in histopathological images from distinct classes. ALSF is evaluated on two challenging databases: (1) kidney tissue images provided by the Animal Diagnosis Lab (ADL) at the Pennsylvania State University and (2) brain tumor images from The Cancer Genome Atlas (TCGA) database. Experimental results confirm that ALSF can offer benefits over state of the art alternatives.
Tractable Transmit MIMO Beampattern Design Under a Constant Modulus Constraint

Multiple-input multiple-output (MIMO) radar systems allow each antenna element to transmit a different waveform. This waveform diversity can be exploited to enhance the beampattern design, in particular, effective management of radar radiation power in directions of interest. We address the problem of designing a beampattern for MIMO radar, which in turn is determined by the transmit waveform. While unconstrained design is straightforward, a key open challenge is enforcing the constant modulus constraint on the radar waveform. It is well known that the problem of minimizing deviation of the designed beampattern from an idealized one subject to the constant modulus constraint constitutes a hard nonconvex problem. Existing methods that address constant modulus invariably lead to a stiff tradeoff between analytical tractability (achieved by relaxations and approximations) and realistic design that exactly achieves constant modulus but is computationally burdensome. A new approach is proposed in our paper, which involves solving a sequence of convex equality constrained quadratic programs, each of which has a closed form solution and such that constant modulus is achieved at convergence. We further prove that the converged solution satisfies the Karush–Kuhn–Tucker optimality conditions of the aforementioned hard nonconvex problem. We evaluate the proposed successive closed forms (SCF) algorithm against the state-of-the art MIMO beampattern design techniques in both narrowband and wideband setups and show that the SCF breaks the tradeoff between desirable performance and the associated computation cost.
Hierarchical sparse modeling using Spike and Slab priors

Sparse modeling has demonstrated its superior performances in many applications. Compared to optimization based approaches, Bayesian sparse modeling generally provides a more sparse result with a knowledge of confidence. Using the Spike and Slab priors, we propose the hierarchical sparse models for the scenario of single task and multitask - Hi-BCS and CHi-BCS. We draw the connections of these two methods to their optimization based counterparts and use expectation propagation for inference. The experiment results using synthetic and real data demonstrate that the performance of Hi-BCS and Chi-BCS are comparable or better than their optimization based counterparts.
A comparative study of basis selection techniques for automatic target recognition

Often in automatic target recognition (ATR) problems, a small number of representative features that encapsulate image information are usually extracted from the target images prior to the actual classification procedure. In literature, principal component analysis (PCA) is one of the most widely used feature extraction techniques. In this paper, we investigate the capability of basis representations to encode discriminative information for target classification using synthetic aperture radar (SAR) imagery. Specifically, we consider the two different scenarios of shared basis built using all available training and class-specific basis using training from each class separately. We compare the traditional PCA-based technique with basis representations constructed using oriented PCA and non-negative matrix approximations (NNMA). Experiments on the benchmark MSTAR database reveal the merits of basis selection techniques that can model imaging physics more closely and can capture inter-class variability, in addition to identifying a trade-off between classification performance and availability of training.
SQR: Successive QCQP refinement for MIMO radar waveform design under practical constraints

We address the problem of designing a waveform for Multiple-Input Multiple-Output (MIMO) radar under important practical constraints, namely the constant modulus and the waveform similarity constraints. Incorporating these constraints in an analytically tractable manner continues to be longstanding open challenge. This is because the optimization problem that results from Signal to Interference plus Noise Ratio (SINR) maximization subject to these constraints is a hard non-convex problem. We develop a new analytical approach that involves solving a sequence of convex Quadratic Constrained Quadratic Programming (QCQP) problems, which we prove converges to a sub-optimal solution. We call the method Successive QCQP Refinement (SQR). We evaluate SQR against state of the art in its SINR performance for a practical scenario and show that it outperforms existing methods without incurring a significant computational burden.
Sparsity-Based Color Image Super Resolution via Exploiting Cross Channel Constraints

Sparsity constrained single image super-resolution (SR) 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 using the coefficients of this representation to generate the high-resolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for SR focus on the luminance channel information and do not capture interactions between color channels. In this paper, we extend sparsity-based SR to multiple color channels by taking the color information into account. Edge similarities amongst RGB 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. Moreover, to fully exploit the complementary information among color channels, a dictionary learning method is also proposed specifically to learn color dictionaries that encourage edge similarities. Merits of the proposed method over state of the art are demonstrated both visually and quantitatively using image quality metrics.
Deep Wavelet Prediction for Image Super-Resolution

Recent advances have seen a surge of deep learning approaches for image super-resolution. Invariably, a network, e.g. a deep convolutional neural network (CNN) or auto-encoder is trained to learn the relationship between low and high-resolution image patches. Recognizing that a wavelet transform provides a "coarse" as well as "detail" separation of image content, we design a deep CNN to predict the "missing details" of wavelet coefficients of the low-resolution images to obtain the Super-Resolution (SR) results, which we name Deep Wavelet Super-Resolution (DWSR). Out network is trained in the wavelet domain with four input and output channels respectively. The input comprises of 4 sub-bands of the low-resolution wavelet coefficients and outputs are residuals (missing details) of 4 sub-bands of high-resolution wavelet coefficients. Wavelet coefficients and wavelet residuals are used as input and outputs of our network to further enhance the sparsity of activation maps. A key benefit of such a design is that it greatly reduces the training burden of learning the network that reconstructs low frequency details. The output prediction is added to the input to form the final SR wavelet coefficients. Then the inverse 2d discrete wavelet transformation is applied to transform the predicted details and generate the SR results. We show that DWSR is computationally simpler and yet produces competitive and often better results than state-of-the-art alternatives.
Sparsity constrained estimation via spike and slab priors

We address sparse signal, i.e. image recovery in a Bayesian estimation framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. In particular, we focus on the popular spike and slab prior which is considered the gold standard in the statistics literature. The optimization problem resulting from this model has broad applicability in recovery, regression and classification problems and is known to be a hard non-convex problem whose 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. Applications will be considered in image classification as well as image reconstruction.
Sparsity-based face recognition using discriminative graphical models

A key recent advance in face recognition which models a test face image as a sparse linear combination of training face images has demonstrated robustness against a variety of distortions, albeit under the restrictive assumption of perfect image registration. To overcome this misalignment problem, we propose a graphical learning framework for robust automatic face recognition, utilizing sparse signal representations from face images as features for classification. Our approach combines two key ideas from recent work in: (i) locally adaptive block-based sparsity for face recognition, and (ii) discriminative learning of graphical models. In particular, we learn discriminative graphs on sparse representations obtained from distinct local slices of a face. The graphical models are learnt in a manner such that conditional correlations between these sparse features are first discovered (in the training phase), and subsequently exploited to bring about significant improvements in recognition rates. Experimental results show that the complementary merits of existing sparsity-based face recognition techniques - which use class specific reconstruction error as a recognition statistic - in comparison with our proposed approach can further be mined into building a powerful meta-classifier for face recognition.
Fast Low-Rank Shared Dictionary Learning for Image Classification

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.