nils-herrmann/clean_scirepeval huggingface dataset:
Task-driven dictionary learning for hyperspectral image classification with structured sparsity priors

In hyperspectral pixel classification, previous research have shown that the sparse representation classifier can achieve a better performance when exploiting the neighboring test pixels through enforcing different structured sparsity priors. In this paper, we propose a supervised sparse-representation-based dictionary learning method with joint or Laplacian s-parsity priors. The proposed method has numerous advantages over the existing dictionary learning techniques. It uses a structured sparsity and provides a more robust and stable sparse coefficients. Besides, it is capable of reducing the classification error by jointly optimizing the dictionary and the classifier's parameters during the dictionary training stage.
Joint-Structured-Sparsity-Based Classification for Multiple-Measurement Transient Acoustic Signals

This paper investigates the joint-structured-sparsity-based methods for transient acoustic signal classification with multiple measurements. By joint structured sparsity, we not only use the sparsity prior for each measurement but we also exploit the structural information across the sparse representation vectors of multiple measurements. Several different sparse prior models are investigated in this paper to exploit the correlations among the multiple measurements with the notion of the joint structured sparsity for improving the classification accuracy. Specifically, we propose models with the joint structured sparsity under different assumptions: same sparse code model, common sparse pattern model, and a newly proposed joint dynamic sparse model. For the joint dynamic sparse model, we also develop an efficient greedy algorithm to solve it. Extensive experiments are carried out on real acoustic data sets, and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.
Automatic target recognition using modularly cascaded vector quantizers and multilayer perceptrons

An automatic target recognition classifier is constructed of a set of vector quantizers (VQs) and multilayer perceptrons (MLPs) that are modularly cascaded. A dedicated VQ codebook is constructed for each target at a specific range of aspects. Each codebook is a set of block feature templates that are iteratively adapted to represent a particular target at a specific range of aspects. These templates are further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory power. The mean squared errors resulting from matching the input image with the block templates in each each codebook are input to an array of window MLPs (WMLPs). Each WMLP is trained to recognize its intended-target at a specific range of aspects. The outputs of the WMLPs are manipulated and fed into a target MLP (TMLP) that produces the final recognition results. A recognition rate of 65.3 percent is achieved on a highly cluttered test set.
Dynamic finite-state vector quantization of digital images

A vector quantization (VQ) scheme with finite memory called dynamic finite-state vector quantization (DFSVQ) is presented. The encoder consists of a large codebook, so called super-codebook, where for each input vector a fixed number of its codevectors are chosen to generate a much smaller codebook (sub-codebook). This sub-codebook represents the best matching codevectors that could be found in the super-codebook for encoding the current input vector. The choice for the codevectors in the sub-codebook is based on the information obtained from the previously encoded blocks where directional conditional block probability (histogram) matrices are used in the selection of the codevectors. The index of the best matching codevector in the sub-codebook is transmitted to the receiver. An adaptive DFSVQ scheme is also proposed in which, when encoding an input vector, first the sub-codebook is searched for a matching codevector to satisfy a pre-specified waveform distortion. If such a codevector is not found in tile current sub-codebook then the whole super-codebook is checked for a better match. If a better match is found then a signaling flag along with the corresponding index of the codevector is transmitted to the receiver. Both the DFSVQ encoder and its adaptive version are implemented. Experimental results for several monochrome images with a super-codebook size of 256 or 512 and different sub-codebook sizes are presented.
Kernel Eigenspace Separation Transform for Subspace Anomaly Detection in Hyperspectral Imagery

This letter proposes a nonlinear version of the eigenspace separation transform (EST) for subspace anomaly detection in hyperspectral imaging. The EST is defined in terms of the eigenvectors of the difference correlation matrix (DCOR) obtained using the data from the two classes. Using ideas found in the machine learning literature (i.e., the kernel trick), a nonlinear version-kernel EST (KEST)-is achieved by expressing the DCOR in terms of dot products in feature space and replacing all dot products with a Mercer kernel function that is defined in terms of input data space. Experimental results indicate that KEST outperforms many other commonly used subspace anomaly detection algorithms.
Collaborative Multi-Sensor Classification Via Sparsity-Based Representation

In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations. We also robustify our models to deal with the presence of sparse noise and low-rank interference signals. Specifically, we demonstrate that incorporating the noise or interference signal as a low-rank component in our models is essential in a multi-sensor classification problem when multiple co-located sources/sensors simultaneously record the same physical event. We further extend our frameworks to kernelized models which rely on sparsely representing a test sample in terms of all the training samples in a feature space induced by a kernel function. A fast and efficient algorithm based on alternative direction method is proposed where its convergence to an optimal solution is guaranteed. Extensive experiments are conducted on several real multi-sensor data sets and results are compared with the conventional classifiers to verify the effectiveness of the proposed methods.
Hierarchical block truncation coding of digital HDTV images

A source coding technique for compressing HDTV (high-definition television) digital images for B-ISDN (broadband integrated services digital network) visual services is presented. The proposed technique is called hierarchical block truncation coding (HBTC). It is a combination of the block truncation coding (BTC) technique and a quadtree segmentation method. The quadtree segmentation is used to decompose an image into homogeneous regions so that the BTC method can exploit the nonstationarity of the image data. Bit rates around 1.46 b/pixel are achieved with this method. Experimental results for several HDTV images are presented. It is shown that greater image compression can be achieved by using a hierarchical BTC method in conjunction with quadtree segmentation, than by applying BTC to an image with uniform block divisions, since the correlation of uniform areas is exploited. This is especially true for appropriate images such as HDTV with large uniform areas.
Task-driven dictionary learning with different Laplacian priors for hyperspectral image classification

Task-driven dictionary learning (TDDL) has shown great success in many classification applications. However, the performance of TDDL is limited by the challenging properties of hyperspectral images (HSI). Fortunately, previous research has made significant progress in HSI classification by enforcing various structured sparsity constraints (priors) on the TDDL-based model. In this paper, we extend some previous work by relaxing the structured sparsity priors and make the model become more flexible and powerful. Specifically, we add class label Laplacian sparsity constraints in two different places, either on the sparse code or on the classifier outputs. Experimental results on widely used datasets shown improvement in performance compared to current state-of-the-art approaches.
Hyperspectral target detection using kernel matched subspace detector

In this paper we present a nonlinear realization of a subspace signal detection approach based on the generalized likelihood ratio test (GLRT) - so called matched subspace detectors (MSD). The linear model for MSD is first extended to a high, possibly infinite, dimensional feature space and then the corresponding nonlinear GLRT expression is obtained. In order to address the intractability of the GLRT in the nonlinear feature space we kernelize the nonlinear GLRT using kernel eigenvector representations as well as the kernel trick where dot products in the nonlinear feature space are implicitly computed by kernels. The proposed kernel-based nonlinear detector, so called kernel matched subspace detector (KMSD), is applied to a given hyperspectral imagery - HYDICE (hyperspectral digital imagery collection experiment) images - to detect targets of interest. KMSD showed superior detection performance over MSD for the HYDICE images tested in this paper.
Simultaneous Joint Sparsity Model for Target Detection in Hyperspectral Imagery

This letter proposes a simultaneous joint sparsity model for target detection in hyperspectral imagery (HSI). The key innovative idea here is that hyperspectral pixels within a small neighborhood in the test image can be simultaneously represented by a linear combination of a few common training samples but weighted with a different set of coefficients for each pixel. The joint sparsity model automatically incorporates the interpixel correlation within the HSI by assuming that neighboring pixels usually consist of similar materials. The sparse representations of the neighboring pixels are obtained by simultaneously decomposing the pixels over a given dictionary consisting of training samples of both the target and background classes. The recovered sparse coefficient vectors are then directly used for determining the label of the test pixels. Simulation results show that the proposed algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, and adaptive subspace detectors, as well as binary classifiers such as support vector machines.
Learning to Classify With Possible Sensor Failures

In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization error of the classifier on noncorrupted measurements while controlling the false alarm rate associated with anomalous samples. By incorporating a nonparametric regularizer based on an empirical entropy estimator, we propose a geometric-entropy-minimization regularized maximum entropy discrimination (GEM-MED) method to learn to classify and detect anomalies in a joint manner. We demonstrate using simulated data and a real multimodal data set. Our GEM-MED method can yield improved performance over previous robust classification methods in terms of both classification accuracy and anomaly detection rate.
Multi-view face recognition via joint dynamic sparse representation

We consider the problem of automatically recognizing a human face from its multi-view images with unconstrained poses and illuminations. We formulate the multi-view face recognition problem as that of classifying among several multi-input (views) regression models by using a novel joint dynamic sparse representation method which exploits jointly the inter-correlation among all the multi-view images in order to make a decision. Extensive experiments on CMU Multi-PIE face database are conducted to verify the efficacy of the proposed method.
Quality-Based Multimodal Classification Using Tree-Structured Sparsity

Recent studies have demonstrated advantages of information fusion based on sparsity models for multimodal classification. Among several sparsity models, tree-structured sparsity provides a flexible framework for extraction of cross-correlated information from different sources and for enforcing group sparsity at multiple granularities. However, the existing algorithm only solves an approximated version of the cost functional and the resulting solution is not necessarily sparse at group levels. This paper reformulates the tree-structured sparse model for multimodal classification task. An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information. In addition, a (fuzzy-set-theoretic) possibilistic scheme is proposed to weight the available modalities, based on their respective reliability, in a joint optimization problem for finding the sparsity codes. This approach provides a general framework for quality-based fusion that offers added robustness to several sparsity-based multimodal classification algorithms. To demonstrate their efficacy, the proposed methods are evaluated on three different applications - multiview face recognition, multimodal face recognition, and target classification.
A multiprocessor system for interframe hierarchical address-vector quantization

The authors report on an ongoing design and performance evaluation of an interframe hierarchical address-vector quantization (IHAVQ) system for coding a sequence of moving images. The image coding system exploits the inherent parallelism of the IHAVQ algorithm by using a tree-structured multiprocessor architecture for distributed motion-compensated image data. An implementation of the system using three TM5320C25 DSP microprocessors is described, and its performance is analyzed.<<ETX>>
An efficient Euclidean distance computation for vector quantization using a truncated look-up table

Vector quantizer (VQ) encoders generally use Euclidean distance measure to encode the vectors. The major computation in the Euclidean distance is square (multiplication operation) of the difference between the vector components. This article explores Euclidean distance computation and introduces a new technique which uses a truncated look-up table (LUT) to store a small set of repeatedly generated scalars. Specifically, for numbers represented by m bits, this technique requires to store only 2/sup m/ product terms instead of 2/sup m//spl times/2/sup m/ product terms needed to store in a conventional LUT.
Very low bit rate video codec using variable blocksize entropy-constrained residual vector quantization

In this paper we study coding of video sequences at very low bitrates using an entropy-constrained variable block size residual vector quantizer. For a given complexity, it is well understood that structured vector quantizers perform better than unstructured and unconstrained vector quantizers. A combination of structured vector quantizers is used in this paper to encode the video sequences. The proposed codec uses a Discrete Cosine Transform (DCT) based transform vector quantizer and a variable-rate residual vector quantizer in cascade. The transform-VQ captures the low-frequency information using only a small portion of the bit-budget, while the later stage residual VQ captures the high-frequency information using the remaining bits. A strategy to adaptively refine only areas of high activity using recursive decomposition and selective refinement is used in the later stages. An entropy constraint is used to modify the codebooks to allow better entropy coding of the indices. Performance of the proposed codec is evaluated and compared with the H.263 based codec. Experimental results show not only better quantitative performance but also significantly better perceptual quality.
Close the loop: Joint blind image restoration and recognition with sparse representation prior

Most previous visual recognition systems simply assume ideal inputs without real-world degradations, such as low resolution, motion blur and out-of-focus blur. In presence of such unknown degradations, the conventional approach first resorts to blind image restoration and then feeds the restored image into a classifier. Treating restoration and recognition separately, such a straightforward approach, however, suffers greatly from the defective output of the ill-posed blind image restoration. In this paper, we present a joint blind image restoration and recognition method based on the sparse representation prior to handle the challenging problem of face recognition from low-quality images, where the degradation model is realistic and totally unknown. The sparse representation prior states that the degraded input image, if correctly restored, will have a good sparse representation in terms of the training set, which indicates the identity of the test image. The proposed algorithm achieves simultaneous restoration and recognition by iteratively solving the blind image restoration in pursuit of the sparest representation for recognition. Based on such a sparse representation prior, we demonstrate that the image restoration task and the recognition task can benefit greatly from each other. Extensive experiments on face datasets under various degradations are carried out and the results of our joint model shows significant improvements over conventional methods of treating the two tasks independently.
Learning to classify with possible sensor failures

In this paper, we propose an efficient algorithm to train a robust large-margin classifier, when corrupt measurements caused by sensor failure might be present in the training set. By incorporating a non-parametric prior based on the empirical distribution of the training data, we propose a Geometric-Entropy-Minimization regularized Maximum Entropy Discrimination (GEM-MED) method to perform classification and anomaly detection in a joint manner. We demonstrate that our proposed method can yield improved performance over previous robust classification methods in terms of both classification accuracy and anomaly detection rate using simulated data and real footstep data.
Resource-aware fusion in multimodal sensing under network constraints

Due to insufficient network infrastructure, tactical networks suffer the imbalance between network resource supply and transmission demands generated by deployed sensing devices. On top of that, those sensing devices are in multiple modalities with the need of fusing diverse inputs into unified formats to provide answers to query tasks. We addressed issues under network resource constraints on exploiting multiple distinctive sensing modalities. Then, we proposed a fusion framework to aggregate various types of sensing inputs, and an updating algorithm to adaptively schedule resource, e.g. bandwidth, to most contributing devices. With the adjustment of mapping functions which get bandwidth consumption bounded by the threshold, our algorithm keeps updating the combination of bandwidth shares to suit for streaming events as well as different query tasks. Taking multimodal sensing into account, our elementary fusion framework may shed light on deeper exploration on the correlation among variety of sensing inputs, especially under resource-constrained circumstances.
Predictive residual vector quantization

Presents a new vector quantization technique, called predictive residual vector quantization (PRVQ), which combines the concepts of predictive vector quantization (PVQ) and residual vector quantization (RVQ) to implement a high performance VQ scheme with low search complexity. A major task in the PRVQ design is the joint optimization of the vector predictor and the RVQ codebooks. In order to achieve this, a constrained optimization technique is introduced which is compared with a jointly designed technique and a closed loop design technique. Simulation results show the superiority of the proposed PRVQ scheme over the equivalent RVQ, PVQ and an unconstrained VQ scheme. The proposed PRVQ scheme gives the best performance when the predictor and all the stage quantizers are jointly optimized.<<ETX>>
Neural network vector predictors with application to image coding

A vector predictor is an integral part of the predictive vector quantization (PVQ) scheme. The performance of a predictor deteriorates as the vector dimension (block size) is increased. This makes it necessary to investigate new design techniques in order to design a vector predictor which gives better performance when compared to a conventional vector predictor. This paper investigates several neural network configurations which can be employed in order to design a vector predictor. The following architectures are investigated: (a) multilayer perceptron, (b) functional link network, and (c) radial basis function network. The performance of the above mentioned neural network vector predictors is evaluated and compared with that of a linear vector predictor.
Scalar-vector quantization of medical images

A new coding scheme based on the scalar-vector quantizer (SVQ) is developed for compression of medical images. The SVQ is a fixed rate encoder and its rate-distortion performance is close to that of optimal entropy-constrained scalar quantizers (ECSQs) for memoryless sources. The use of a fixed-rate quantizer is expected to eliminate some of the complexity of using variable-length scalar quantizers. When transmission of images over noisy channels is considered, our coding scheme does not suffer from error propagation that is typical of coding schemes using variable-length codes. For a set of magnetic resonance (MR) images, coding results obtained from SVQ and ECSQ at low bit rates are indistinguishable. Furthermore, our encoded images are perceptually indistinguishable from the original when displayed on a monitor. This makes our SVQ-based coder an attractive compression scheme for picture archiving and communication systems (PACS). PACS are currently under study for use in an all-digital radiology environment in hospitals, where reliable transmission, storage, and high fidelity reconstruction of images are desired.
Task-driven deep transfer learning for image classification

Transfer learning tends to be a powerful tool that can mitigate the divergence across different domains through knowledge transfer. Recent research efforts on transfer learning have exploited deep neural network (NN) structures for discriminative feature representation to better tackle cross-domain disparity. However, few of these techniques are able to jointly learn deep features and train a classifier in a unified transfer learning framework. To this end, we design a task-driven deep transfer learning framework for image classification, where the deep feature and classifier are obtained simultaneously for optimal classification performance. Therefore, the proposed deep structure can generate more discriminative features by using the classifier performance as a guide. Furthermore, the classifier performance is increased since it is optimized on a more discriminative deep feature. The developed supervised formulation is a task-driven scheme, which will provide better learned features for the classification task. By giving pseudo labels for target data, we can facilitate the knowledge transfer from source to target through the deep structures. Experimental results witness the superiority of our proposed algorithm by comparing with other ones.