nils-herrmann/clean_scirepeval huggingface dataset:
Multichannel transient acoustic signal classification using task-driven dictionary with joint sparsity and beamforming

We are interested in a multichannel transient acoustic signal classification task which suffers from additive/convolutionary noise corruption. To address this problem, we propose a double-scheme classifier that takes the advantage of multichannel data to improve noise robustness. Both schemes adopt task-driven dictionary learning as the basic framework, and exploit multichannel data at different levels - scheme 1 imposes joint sparsity constraint while learning the dictionary and classifier; scheme 2 adopts beamforming at signal formation level. In addition, matched filter and robust ceptral coefficients are applied to improve noise robustness of the input feature. Experiments show that the proposed classifier significantly outperforms the baseline algorithms.
Random projection as dimensionality reduction and its effect on classical target recognition and anomaly detection techniques

There is usually a large amount of redundancy in hyperspectral pixels as they are acquired in hundreds of narrow and continuous spectral bands. Numerous techniques have been proposed to reduce the dimensionality of hyperspectral data in order to improve both computational and memory efficiency. In this paper, we explore the effect of random projection as a dimensionality reduction method on the performance of classical target detection techniques for hyper-spectral images. Specifically, each spectral pixel is projected onto a measurement space with a much smaller dimensionality by a linear transformation represented by a matrix whose entries are randomly generated. The detectors are then applied to the measurement vectors to detect the targets of interests. The detection performances are compared to those obtained from the entire spectrum by the receiver operating characteristics curves. Experimental results demonstrate that only a small number of measurements are necessary to achieve detection performance comparable to that obtained by exploiting the full-spectrum pixels.
Predictive residual vector quantization [image coding]

This paper presents a new vector quantization technique called predictive residual vector quantization (PRVQ). It combines the concepts of predictive vector quantization (PVQ) and residual vector quantization (RVQ) to implement a high performance VQ scheme with low search complexity. The proposed PRVQ consists of a vector predictor, designed by a multilayer perceptron, and an RVQ that is designed by a multilayer competitive neural network. A major task in our proposed PRVQ design is the joint optimization of the vector predictor and the RVQ codebooks. In order to achieve this, a new design based on the neural network learning algorithm is introduced. This technique is basically a nonlinear constrained optimization where each constituent component of the PRVQ scheme is optimized by minimizing an appropriate stage error function with a constraint on the overall error. This technique makes use of a Lagrangian formulation and iteratively solves a Lagrangian error function to obtain a locally optimal solution. This approach is then compared to a jointly designed and a closed-loop design approach. In the jointly designed approach, the predictor and quantizers are jointly optimized by minimizing only the overall error. In the closed-loop design, however, a predictor is first implemented; then the stage quantizers are optimized for this predictor in a stage-by-stage fashion. Simulation results show that the proposed PRVQ scheme outperforms the equivalent RVQ (operating at the same bit rate) and the unconstrained VQ by 2 and 1.7 dB, respectively. Furthermore, the proposed PRVQ outperforms the PVQ in the rate-distortion sense with significantly lower codebook search complexity.
Kernel multi-metric learning for multi-channel transient acoustic signal classification

In this paper, we propose a kernel multi-metric learning algorithm for multi-channel transient acoustic signal classification. The proposed method learns a set of metrics jointly for multi-channel transient acoustic signals in a kernel-induced feature space to exploit the non-linearity of the data for improving the classification performance. An effective algorithm is developed for the task of learning multiple metrics in the kernel space. By learning the multiple metrics jointly within a single unified optimization framework, we can learn better metrics to integrate the multiple channels of the signal for a joint classification. Experimental results compared with classical as well as recent algorithms on real-world acoustic datasets verified the effectiveness of the proposed method.
Evaluation of design parameters for a cache vector quantization system

Observation of the usage of codevectors in vector quantization (VQ) reveals the locality of the reference, both temporally and spatially. Cache VQ takes advantage of such property by providing a large main codebook for quality concerns and a much smaller cache codebook for compression and computational concerns. We evaluate some design parameters for the cache VQ system. Our experimental results show that for various combinations of cache sizes, distortion thresholds, and line sizes, bit ratios ranging from 60% to 97% can be achieved by the cache VQ, with PSNRs between 33 dB and 38 dB and with codevector dimensions of 4 and 16. The corresponding compression ratio without entropy coding ranges from 6:1 to 24:1. These results clearly indicate that a very good performance in terms of time as well as fidelity and bit rate can be realized with the cache VQ.<<ETX>>
Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation

Sparsity-based unmixing algorithms, exploiting the sparseness property of the abundances, have recently been proposed with promising performances. However, these algorithms are developed for the linear mixture model (LMM), which cannot effectively handle the nonlinear effects. In this paper, we extend the current sparse regression methods for the LMM to bilinear mixture models (BMMs), where the BMMs introduce additional bilinear terms in the LMM in order to model second-order photon scattering effects. To solve the abundance estimation problem for the BMMs, we propose to perform a sparsity-based abundance estimation by using two dictionaries: a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible second-order endmember interaction components. Then, the abundance values can be estimated from the sparse codes associated with the linear dictionary. Moreover, to exploit the spatial data structure where the adjacent pixels are usually homogeneous and are often mixtures of the same materials, we first employ the joint-sparsity (row-sparsity) model to enforce structured sparsity on the abundance coefficients. However, the joint-sparsity model is often a strict assumption, which might cause some aliasing artifacts for the pixels that lie on the boundaries of different materials. To deal with this problem, the low-rank-representation model, which seeks the lowest rank representation of the data, is further introduced to better capture the spatial data structure. Our simulation results demonstrate that the proposed algorithms provide much enhanced performance compared with state-of-the-art algorithms.
Very Low Bit-Rate Video Coding Using Variable Block-Size Entropy-Constrained Residual Vector Quantizers

We present a practical video coding algorithm for use at very low bit rates. For efficient coding at very low bit rates, it is important to intelligently allocate bits within a frame, and so a powerful variable-rate algorithm is required. We use vector quantization to encode the motion-compensated residue signal in an H.263-like framework. 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 our work to encode the video sequences. The proposed codec is a multistage residual vector quantizer, with transform vector quantizers in the initial stages. 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. We used a strategy to adaptively refine only areas of high activity, using recursive decomposition and selective refinement in the later stages. An entropy constraint was used to modify the codebooks to allow better entropy coding of the indexes. We evaluate the performance of the proposed codec, and compare this data with the performance of the H.263-based codec. Experimental results show that the proposed codec delivered significantly better perceptual quality along with better quantitative performance.
Automatic target recognition based on simultaneous sparse representation

In this paper, an automatic target recognition algorithm is presented based on a framework for learning dictionaries for simultaneous sparse signal representation and feature extraction. The dictionary learning algorithm is based on class supervised simultaneous orthogonal matching pursuit while a matching pursuit-based similarity measure is used for classification. We show how the proposed framework can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm using confusion matrices on the well known Comanche forward-looking infrared data set consisting of ten different military targets at different orientations.
Finite state residual vector quantization using tree-structured competitive neural network

The performance of an ordinary vector quantizer (VQ) can be improved by incorporating memory in the VQ scheme. A VQ scheme with finite memory known as finite state vector quantization has been shown to give better performance than the ordinary VQ. The major problems with the FSVQ are the lack of accurate prediction of the current state, the state codebook design, and the amount of memory required to store all the state codebooks. The paper presents a new FSVQ scheme called finite-state residual vector quantization (FSRVQ) in which a neural network based state prediction is used. Furthermore, a novel tree-structured competitive neural network is used to jointly design the next-state and the state codebooks for the proposed FSRVQ. Simulation results show that the new scheme gives better performance with significant reduction in the memory requirement when compared to the conventional FSVQ schemes.
Object recognition by a Hopfield neural network

A model-based recognition method is introduced which is formulated as an optimization problem. An energy function is derived which represents the constraints on the best solution in order to find the best match. A two-dimensional binary Hopfield neural network is implemented to minimize the energy function. The state of each neuron in the Hopfield network represents the possibility of a match between a node in the model graph and a node in the scene graph.<<ETX>>
Sparse Representation for Target Detection in Hyperspectral Imagery

In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI). This algorithm is based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented as a sparse linear combination of the training samples. The sparse representation (a sparse vector corresponding to the linear combination of a few selected training samples) of a test sample can be recovered by solving an l0-norm minimization problem. With the recent development of the compressed sensing theory, such minimization problem can be recast as a standard linear programming problem or efficiently approximated by greedy pursuit algorithms. Once the sparse vector is obtained, the class of the test sample can be determined by the characteristics of the sparse vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that in HSI the neighboring pixels have a similar spectral characteristic (smoothness). In our proposed algorithm, a smoothness constraint is also imposed by forcing the vector Laplacian at each reconstructed pixel to be minimum all the time within the minimization process. The proposed sparsity-based algorithm is applied to several hyperspectral imagery to detect targets of interest. Simulation results show that our algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, adaptive subspace detectors, as well as binary classifiers such as support vector machines.
Classification for hyperspectral imagery based on sparse representation

A new sparsity-based classification algorithm for hyperspectral imagery is proposed in this paper. This algorithm is based on the assumption that the spectral signatures of pixels in the same class lie in a low-dimensional subspace and thus a test sample can be represented by a sparse linear combination of the training samples. The sparse representation is recovered by solving a constrained optimization and it directly determines the class label of the test sample. In addition to the constraints on sparsity and reconstruction accuracy, the smoothness of hyperspectral images across neighboring pixels is also exploited by forcing the Laplacian of the reconstructed image to be minimum in the optimization process. Various sparse recovery techniques are applied to solve the optimization problem and their performances are compared against the widely used Support Vector Machine classifier. Simulation results show that the proposed algorithm yields a favorable performance over the support vector machines.
A Max-Margin Perspective on Sparse Representation-Based Classification

Sparse Representation-based Classification (SRC) is a powerful tool in distinguishing signal categories which lie on different subspaces. Despite its wide application to visual recognition tasks, current understanding of SRC is solely based on a reconstructive perspective, which neither offers any guarantee on its classification performance nor provides any insight on how to design a discriminative dictionary for SRC. In this paper, we present a novel perspective towards SRC and interpret it as a margin classifier. The decision boundary and margin of SRC are analyzed in local regions where the support of sparse code is stable. Based on the derived margin, we propose a hinge loss function as the gauge for the classification performance of SRC. A stochastic gradient descent algorithm is implemented to maximize the margin of SRC and obtain more discriminative dictionaries. Experiments validate the effectiveness of the proposed approach in predicting classification performance and improving dictionary quality over reconstructive ones. Classification results competitive with other state-of-the-art sparse coding methods are reported on several data sets.
An application of wavelet-based vector quantization in target recognition

An automatic target recognition (ATR) classifier is constructed that uses a set of dedicated vector quantizers (VQs). The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition splits the enlarged extraction into several subbands. A dedicated VQ codebook is generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization algorithm that enhances their discriminatory characteristics.
Asymptotical analysis of a modular neural network

Modular neural networks have been used in several applications because of their superiority over a single neural network in terms of faster learning, proper data representation, and feasibility of hardware implementation. This paper presents an asymptotical performance analysis showing that the performance of a modular neural network is always better than or as good as that of a single neural network when both neural networks are optimized. The minimum mean square error (MSE) that can be achieved by a modular neural network is also obtained.
Constrained gradient descent algorithm for residual vector quantizer design

Residual vector quantizers have been proposed to overcome the search complexity of regular single stage vector quantizers. We present a design algorithm for residual vector quantizer codebooks. An attempt is made to make full use of the sequential search nature of the encoding process. An error energy is formulated based on the distortion criterion with a constraint imposed to optimize for the sequential search. The codebook design is formulated as multidimensional minimization problem, where the error energy is minimized to obtain the required codebooks. The proposed algorithm is based on the gradient descent algorithm.<<ETX>>
Kernel spectral matched filter for hyperspectral target detection

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery. The proposed spectral matched filter is defined in a kernel feature space which is equivalent to a nonlinear matched filter in the original input space. This nonlinear spectral matched filter is based on the notion that performing matched filtering in the high dimensional feature space increases the separability of spectral data mainly because it exploits the higher order correlation between the spectral bands. It is also shown that the nonlinear spectral matched filter can easily be implemented in terms of kernel functions using the so called kernel trick property of the Mercer kernels. The kernel version of the nonlinear spectral matched filter is implemented and simulation results on hyperspectral imagery are shown to outperform the linear version.
Clutter rejection in FLIR imagery using spatially-varying adaptive filtering

This paper introduces an adaptive method for rejecting clutter in forward-looking infra-red (FLIR) imagery. In this approach, a spatially-varying two-dimensional adaptive filtering method is developed to identify a metric distance (error energy) between a test scene and a library of reference man-made targets (trucks, tanks, etc.). The function of the 2D spatially-varying adaptive filter is to compensate for: a) variations of image point response (IPR) of a FLIR sensor; b) variations of heat distribution on the test and reference targets; c) subpixel shifts in the relative coordinates of these targets; and d) subtle scaling and rotations. A statistic is constructed that is the least square energy of the error between the test target and its projection into each member of the library of reference target chips based on the above-mentioned spatially-varying 2D adaptive filtering; this database is then used to identify the test chip as a man-made target or clutter. Results with FLIR data of scenes composed of trucks, tanks, and APCs at various angles in different types of clutter environment will be provided.
Modular wavelet-based vector quantization for automatic target recognition

An automatic target recognition (ATR) classifier is constructed that uses a set of dedicated vector quantizers (VQs). The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition splits the enlarged extraction into several subbands A dedicated VQ codebook is generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics. Finally, a path selector is designed to speed up the recognition process with a tolerable degradation in recognition rate.
Kernel adaptive subspace detector for hyperspectral imagery

In this letter, we present a kernel-based nonlinear version of the adaptive subspace detector (ASD) that implicitly detects signals of interest in a high-dimensional (possibly infinite) feature space associated with a particular 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 property of the Mercer kernels. Experimental results based on simulated data and real hyperspectral imagery show that the proposed kernel-based ASD outperforms the conventional ASD and a nonlinear anomaly detector so called the kernel RX-algorithm.
Cellular radio channel assignment using a modified Hopfield network

The channel-assignment problem is important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal channel-assignment problem has become increasingly important. A new channel-assignment algorithm using a modified Hopfield (1985, 1986) neural network is proposed. The channel-assignment problem is formulated as an energy-minimization problem that is implemented by a modified discrete Hopfield network. Also, a new technique to escape the local minima is introduced. In this algorithm, an energy function is derived, and the appropriate interconnection weights between the neurons are specified. The interconnection weights between the neurons are designed in such a way that each neuron receives inhibitory support if the constraint conditions are violated and receives excitatory support if the constraint conditions are satisfied. To escape the local minima, if the number of assigned channels are less than the required channel numbers (RCNs), one or more channels are assigned in addition to already assigned channels such that the total number of assigned channels is the same as the required number of channels in the cell even though the energy is increased. Various initialization techniques, which use the specific characteristics of frequency-assignment problems in cellular radio networks, such as cosite constraint (CSC), adjacent channel constraint (ACC), and cochannel constraint (CCC), and updating methods are investigated. In the previously proposed neural-network approach, some frequencies are fixed to accelerate the convergence time. In our algorithms, no frequency is fixed before the frequency-assignment procedure. This new algorithm, together with the proposed initialization and updating techniques and without fixing frequencies in any cells, has better performance results than the results reported previously utilizing fixed frequencies in certain cells.
Semi-supervised multi-sensor classification via consensus-based Multi-View Maximum Entropy Discrimination

In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples. The problem is formulated under the multi-view learning framework and a Consensus-based Multi-View Maximum Entropy Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the stochastic agreement between multiple classifiers on the unlabeled dataset, the algorithm simultaneously learns multiple high accuracy classifiers. We demonstrate that our proposed method can yield improved performance over previous multi-view learning approaches by comparing performance on three real multi-sensor data sets.