nils-herrmann/clean_scirepeval huggingface dataset:
Lossless image compression using modular differential pulse code modulation

This paper presents a new lossless image compression technique called modular differential pulse code modulation (MDPCM). The proposed technique consists of a VQ classifier and several neural network class predictors. The classifier uses the four previously encoded pixels to identify the class of the current pixel (the pixel to be predicted). The current pixel is then predicted by the corresponding class predictor. Experimental results demonstrate that the proposed technique reduces the bit rate by as much as 10 percent when compared to the lossless JPEG.
Sparsity-based classification of hyperspectral imagery

In this paper, a new sparsity-based classification algorithm for hyperspectral imagery is proposed. This algorithm is based on the concept that a pixel in hyperspectral imagery lies in a low-dimensional subspace and thus can be represented by a sparse linear combination of the training samples. The sparse representation (a sparse vector representing the selected training samples) of a test sample can be recovered by solving a constrained optimization problem. Once the sparse vector is obtained, the class of the test sample can be directly determined by the behavior of the vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that hyperspectral images are usually smooth within a neighborhood. In our proposed algorithm, a smoothness constraint is imposed by forcing the Laplacian of the reconstructed image to be minimum in the optimization process. The proposed sparsity-based algorithm is applied to several hyperspectral imagery to classify the pixels into target and background classes. Simulation results show that our algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, and adaptive subspace detectors.
Residual vector quantization using a multilayer competitive neural network

This paper presents a new technique for designing a jointly optimized residual vector quantizer (RVQ). In conventional stage-by-stage design procedure, each stage codebook is optimized for that particular stage distortion and does not consider the distortion from the subsequent stages. However, the overall performance can be improved if each stage codebook is optimized by minimizing the distortion from the subsequent stage quantizers as well as the distortion from the previous stage quantizers. This can only be achieved when stage codebooks are jointly designed for each other. In this paper, the proposed codebook design procedure is based on a multilayer competitive neural network where each layer of this network represents one stage of the RVQ. The weight connecting these layers form the corresponding stage codebooks of the RVQ. The joint design problem of the RVQ's codebooks (weights of the multilayer competitive neural network) is formulated as a nonlinearly constrained optimization task which is based on a Lagrangian error function. This Lagrangian error function includes all the constraints that are imposed by the joint optimization of the codebooks. The proposed procedure seeks a locally optimal solution by iteratively solving the equations for this Lagrangian error function. Simulation results show an improvement in the performance of an RVQ when designed using the proposed joint optimization technique as compared to the stage-by-stage design, where both generalized Lloyd algorithm (GLA) and the Kohonen learning algorithm (KLA) were used to design each stage codebook independently, as well as the conventional joint-optimization technique.
Rate-constrained modular predictive residual vector quantization of digital images

A novel modular coding paradigm is investigated using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks that are optimized for predicting a particular class of data. The predictor also consists of an integrating unit that mixes the outputs of the expert networks to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (modular PRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same peak signal-to-noise ratio (PSNR), the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.
Kernel sparse representation for hyperspectral target detection

In this paper, we present a nonlinear kernel-based target detection algorithm for hyperspectral images. The proposed approach relies on the sparse representation of an unknown sample with respect to both background and target training samples in a high-dimensional feature space induced by a kernel function. The sparse representation vector can be recovered via a kernelized greedy algorithm, where the kernel trick is used to avoid explicit evaluations of the data in the feature space. The spatial smoothness in hyperspectral images is also taken into account through a kernelized joint sparsity model. The detection decision is then made by comparing the reconstruction accuracy in terms of the background and target sub-dictionaries. The detection algorithm in a high-dimensional feature space implicitly exploits the higher-order structure (correlations) within the data which cannot be captured by a linear model. Therefore, projecting the pixels into a kernel feature space and kernelizing the linear sparse representation model improves the separability between the background and target classes, leading to a more accurate detection performance.
Automatic target recognition using a feature-decomposition and data-decomposition modular neural network

A modular neural network classifier has been applied to the problem of automatic target recognition using forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks. Each neural network makes a decision based on local features extracted from a specific portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. Performance of the classifier is further improved by the use of multiresolution features and by the introduction of a higher level neural network on the top of the individual networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data-decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of real FLIR images.
Infrared-image classification using support vector machines

A target recognition classifier for forward-looking infrared (FUR) imagery is developed. A target class is defined as a set of contiguous target-sensor orientations (aspects) for which the associated FLIR imagery is stationary. We designed four sets of templates for each target class, to represent the overall image as well as three class-dependent subcomponents. The templates are designed by using expansion matching (EXM) filters and the Karhunen-Loeve transform (KLT). The feature vectors obtained with these eigen templates are used in the context of a support vector machine (SVM). The performance of the SVM classifier is presented and compared with other competitive classifiers.
Separated Component-Based Restoration of Speckled SAR Images

Many coherent imaging modalities such as synthetic aperture radar suffer from a multiplicative noise, commonly referred to as speckle, which often makes the interpretation of data difficult. An effective strategy for speckle reduction is to use a dictionary that can sparsely represent the features in the speckled image. However, such approaches fail to capture important salient features such as texture. In this paper, we present a speckle reduction algorithm that handles this issue by formulating the restoration problem so that the structure and texture components can be separately estimated with different dictionaries. To solve this formulation, an iterative algorithm based on surrogate functionals is proposed. Experiments indicate the proposed method performs favorably compared to state-of-the-art speckle reduction methods.
Subband coding with multistage VQ for wireless image communication

A subband multistage vector quantization (SB-MSVQ) coding technique is proposed for wireless image communication. The Digital European Cordless Telecommunications (DECT) system has been used as a wireless communication environment. The impact of channel fading on the SB-MSVQ coding technique is investigated and its performance is compared with that of the baseline JPEG standard. Simulation results show that the proposed technique, SB-MSVQ, is very robust to channel fading errors even at a very low channel power, whereas the baseline JPEG suffers from channel fading errors which are visually very annoying. Due to the inherent structure of SB-MSVQ, the important low frequency information of an image is packed into a small portion of the encoded data. If this portion is received intact by the SB-MSVQ decoder, the overall quality degradation of the reconstructed image is marginal.
Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition

Heterogeneous face recognition is the problem of identifying a person from a face image acquired with a nontraditional sensor by matching it to a visible gallery. Most approaches to this problem involve modeling the relationship between corresponding images from the visible and sensing domains. This is typically done at the patch level and/or with shallow models with the aim to prevent overfitting. In this work, rather than modeling local patches or using a simple model, we propose to use a complex, deep model to learn the relationship between the entirety of cross-modal face images. We describe a deep convolutional neural network based method that leverages a large visible image face dataset to prevent overfitting. We present experimental results on two benchmark datasets showing its effectiveness.
Fusion techniques for automatic target recognition

In this paper, we investigate several fusion techniques for designing a composite classifier to improve the performance (probability of correct classification) of FLIR ATR. In this research, we propose to use four ATR algorithms for fusion. The individual performance of the four contributing algorithms ranges from 73.5% to about 77% of probability of correct classification on the testing set. We propose to use Bayes classifier, committee of experts, stacked-generalization, winner-takes-all, and ranking-based fusion techniques for designing the composite classifiers. The experimental results show an improvement of more than 6.5% over the best individual performance.
Kernel adaptive subspace detector for hyperspectral target detection

In this paper, we present a kernel-based nonlinear version of the adaptive subspace detector (ASD) that detects signals of interest in a high dimensional (possibly infinite) feature space associated with a certain nonlinear mapping. In order to address the high dimensionality of the feature space, ASD is first implicitly formulated in the feature space which is then converted into an expression in terms of kernel functions via the kernel trick of the Mercer kernels. The proposed kernel-based ASD (KASD) exploits the nonlinear correlations between the spectral bands that is ignored by the conventional ASD. Experimental results based on the given hyperspectral image show that the proposed KASD outperforms the conventional ASD.
Multi-observation visual recognition via joint dynamic sparse representation

We address the problem of visual recognition from multiple observations of the same physical object, which can be generated under different conditions, such as frames at different time instances or snapshots from different viewpoints. We formulate the multi-observation visual recognition task as a joint sparse representation model and take advantage of the correlations among the multiple observations for classification using a novel joint dynamic sparsity prior. The proposed joint dynamic sparsity prior promotes shared joint sparsity pattern among the multiple sparse representation vectors at class-level, while allowing distinct sparsity patterns at atom-level within each class in order to facilitate a flexible representation. The proposed method can handle both homogenous as well as heterogenous data within the same framework. Extensive experiments on various visual classification tasks including face recognition and generic object classification demonstrate that the proposed method outperforms existing state-of-the-art methods
Hopfield network for stereo vision correspondence

An optimization approach is used to solve the correspondence problem for a set of features extracted from a pair of stereo images. A cost function is defined to represent the constraints on the solution, which is then mapped onto a two-dimensional Hopfield neural network for minimization. Each neuron in the network represents a possible match between a feature in the left image and one in the right image. Correspondence is achieved by initializing (exciting) each neuron that represents a possible match and then allowing the network to settle down into a stable state. The network uses the initial inputs and the compatibility measures between the matched points to find a stable state.
A multilayer address vector quantization technique

A new vector quantization scheme for encoding monochrome and RGB color images is presented. The technique, called multilayer address vector quantization, is based upon using vector quantization in a multilayer fashion. In the first layer, a standard vector quantization is performed on each block of the image. In the second layer, the resulting vector quantization addresses of the neighboring blocks are merged together to form a new vector which is said to be address vector quantized if this address combination vector can be found in an address codebook. This process of merging four nodes (neighboring addresses) is repeated recursively in a bottom-up hierarchical manner in the subsequent higher layers using the corresponding address codebook in each layer. The address codebook in each layer consists of the most probable address combinations. A three-layer coding system is simulated where the codebook in the first layer is a standard VQ codebook, and the codebooks in the other two layers are address codebooks with codevectors consisting of the addresses of the previous layer codebook. Simulation results demonstrate good quality reproduction at bit rates above 0.23 b/pixel for monochrome images and 0.35 b/pixel for RGB color images.
An adaptive segmentation algorithm using iterative local feature extraction for hyperspectral imagery

We present an adaptive segmentation algorithm based on the iterative use of a modified minimum-distance classifier. Local adaptivity is achieved by gradually updating each class centroid over a local region whose size is reduced progressively during a segmentation process. The proposed method provides improved segmentation performance over template matching segmentation techniques because it adapts to the local context. The proposed algorithm can be applied to virtually any hyperspectral image regardless of size, dimensionality, and spectral sensitivity. Experimental results on a set of visible to near-infrared hyperspectral images using both the proposed algorithm and a standard template matching technique are presented.
Joint Sparse Representation for Robust Multimodal Biometrics Recognition

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.
Sparsity-based fusion of multiple sensors and multiple features for acoustic transients classification

In this paper, we propose a collaborative multi-sensor sparsity-based framework for the classification of acoustic signals which enforces various sparsity constraints such as row-sparsity and group-sparsity across measurements of a single microphone array as well as across multiple arrays, while simultaneously extracting a low-rank interference term. We further extend our framework to incorporate information at the feature level by imposing a joint-structure among multiple feature spaces. A fast and efficient algorithm based on alternative direction method of multiplier (ADMM) is proposed to solve the optimization problems of the models. Extensive experiments are conducted on real data collected by the U. S. Army Research Laboratory for the purpose of identifying weapons from their firing sounds. The classification results are compared to conventional classifiers and deep-learning-based algorithms to verify the efficacy of the proposed methods.
Combination of two learning algorithms for automatic target recognition

Composite classifiers consisting of a number of component classifiers have been designed and evaluated on the problem of automatic target recognition (ATR) using a large set of real forward-looking infrared (FLIR) imagery. Two existing classifiers are used as the building blocks for our composite classifiers. The performance of the proposed composite classifiers are compared based on their classification ability and computational complexity. It is demonstrated that the composite classifier based on a cascade architecture greatly reduces the computational complexity with a statistically insignificant decrease in performance in comparison to standard classifier fusion algorithms.
Opportunistic sensing for object recognition — A unified formulation for dynamic sensor selection and feature extraction

A novel problem of object recognition with dynamically allocated sensing resources is considered in this paper. We call this problem opportunistic sensing since prior knowledge about the correlation between class label and signal distribution is exploited as early as in data acquisition. Two forms of sensing parameters - discrete sensor index and continuous linear measurement vector - are optimized within the same maximum negative entropy framework. The computationally intractable expected entropy is approximated using unscented transform for Gaussian models, and we solve the problem using a gradient-based method. Our formulation is theoretically shown to be closely related to the maximum mutual information criterion for sensor selection and linear feature extraction techniques such as PCA, LDA, and CCA. The proposed approach is validated on multi-view vehicle classification and face recognition datasets, and remarkable improvement over baseline methods is demonstrated in the experiments.
3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition

Audio–visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this paper. We propose the use of a coupled 3D convolutional neural network (3D CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio–visual streams using the learned multimodal features. The proposed architecture will incorporate both spatial and temporal information jointly to effectively find the correlation between temporal information for different modalities. By using a relatively small network architecture and much smaller data set for training, our proposed method surpasses the performance of the existing similar methods for audio–visual matching, which use 3D CNNs for feature representation. We also demonstrate that an effective pair selection method can significantly increase the performance. The proposed method achieves relative improvements over 20% on the equal error rate and over 7% on the average precision in comparison to the state-of-the-art method.
Hyperspectral Image Classification Using Dictionary-Based Sparse Representation

A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse representation of an unknown pixel is expressed as a sparse vector whose nonzero entries correspond to the weights of the selected training samples. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Two different approaches are proposed to incorporate the contextual information into the sparse recovery optimization problem in order to improve the classification performance. In the first approach, an explicit smoothing constraint is imposed on the problem formulation by forcing the vector Laplacian of the reconstructed image to become zero. In this approach, the reconstructed pixel of interest has similar spectral characteristics to its four nearest neighbors. The second approach is via a joint sparsity model where hyperspectral pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few common training samples, which are weighted with a different set of coefficients for each pixel. The proposed sparsity-based algorithm is applied to several real hyperspectral images for classification. Experimental results show that our algorithm outperforms the classical supervised classifier support vector machines in most cases.
Hyperspectral anomaly detection using kernel RX-algorithm

In this paper we present a nonlinear version of the well-known anomaly detection method, referred to as the RX-algorithm, by extending this algorithm in a feature space associated with the original input space via a certain nonlinear mapping function. An expression for the nonlinear form of the RX-algorithm is derived which is basically intractable mainly due to the high dimensionality of the feature space. We convert the nonlinear RX expression into kernels, which implicitly compute dot products in the nonlinear domain. The proposed kernel RX-algorithm is applied to hyperspectral images for anomaly detection. Improved performance of the kernel RX over the conventional RX is shown for the HYDICE (hyperspectral digital imagery collection experiment) images tested.