nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic address-vector quantisation of RGB colour images

Address-vector quantisation (A-VQ) is a new class of vector quantisation (VQ) technique that exploits the statistical redundancy between the neighbouring blocks, to reduce the bit rate to a much smaller value than a standard vector quantiser can achieve. The underlying idea of A-VQ is that the interblock correlation is exploited by encoding a group of neighbouring VQ addresses together. The VQ addresses are obtained by vector quantisation of each individual block. An address codebook, which consists of a set of address codevectors, is then used to encode the neighbouring VQ addresses together; this process introduces no extra coding error since it is a vector dimensionality reduction process. In the paper, the A-VQ coding technique is extended to encode RGB colour images by utilising the interblock as well as the intercolour correlation. Also, a modified A-VQ technique named dynamic address-vector quantisation (dynamic A-VQ) is introduced for the encoding of colour images. In the dynamic A-VQ, the encoder dynamically tracks the local characteristics of the input image. Experimental results show reconstructed images at bit rates of 0.5–0.6 bits per pixel. The resulting bit rate is less than a standard VQ coding technique by a factor of at least two, with the same quality of reconstructed images.
Joint compression and discrimination algorithm for clutter rejection

Many pattern recognition (ATR) systems perform dimensionality reduction on the input imagery to reduce the requirement for a large number of training samples and high computational cost. Most of the dimensionality reduction techniques seek to optimize only the overall data compression, but not the interclass discriminability. We present a neural-network-based algorithm that simultaneously achieves data compression and target discriminability by adjusting the pre-trained base components to maximize separability between classes. This allows the classifiers to operate at a higher level of efficiency and generalization capability on low-dimensional data. We have applied this technique to the problem of automatic detection of military vehicles in infrared imagery.
An adaptive hierarchical segmentation algorithm based on quadtree decomposition for hyperspectral imagery

We present an adaptive hierarchical segmentation algorithm based on quadtree decomposition and a modified minimum-distance classifier. The proposed algorithm uses quadtree decomposition because this technique can adapt to the local characteristics of the hyperspectral data. A feature vector (i.e., a class centroid) for each material type is recursively estimated and updated, so that increasingly accurate segmentation results are achieved as the decomposition proceeds. The proposed method provides improved segmentation performance over standard template-matching segmentation techniques because it adapts to the local context. It also imposes a spatial smoothness constraint on the pixel classification that provides spatial continuity during the segmentation process. Both the proposed algorithm and a standard template-matching technique were applied to a set of visible to near-infrared hyperspectral images results are presented.
Entropy-constrained predictive residual vector quantization of digital images

A major problem with a VQ based image compression scheme is its codebook search complexity. Recently, a new VQ scheme called predictive residual vector quantizer (PRVQ) was proposed by Rizvi and Nasrabadi (see Proc. Austin), vol.1, p.608-12, Nov. 13-16, 1994) which has a performance very close to that of the predictive vector quantizer (PVQ) with very low search complexity. This paper presents a new variable-rate VQ scheme called entropy-constrained PRVQ (EC-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. The proposed EC-PRVQ is found to give a good rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by the Joint Photographic Experts Group (JPEG). The robustness of EC-PRVQ is demonstrated by encoding several test images taken from outside the training data.
Predictive residual vector quantization

This paper 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 then 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.
Hyperspectral abundance estimation for the generalized bilinear model with joint sparsity constraint

In this paper, we present a novel abundance estimation method for the generalized bilinear model (GBM) via sparse representation for hyperspectral imagery. Because the GBM generalizes the linear mixture model (LMM) by introducing an additional bilinear term, our sparsity-based abundance estimation is performed by utilizing two dictionaries-a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible bilinear interaction components. Because the components within the bilinear term are also linearly combined, by employing a composite dictionary made up by the concatenation of the linear and bilinear dictionaries we can reformulate the bilinear problem in a linear sparse regression framework. In this way, the abundance values are estimated from the sparse codes only associated with the linear dictionary. To further improve the estimation performance, we incorporate the joint-sparsity model to exploit the spatial information in the data. The experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real data.
Semi-supervised Deep Domain Adaptation via Coupled Neural Networks

Domain adaptation is a promising technique when addressing limited or no labeled target data by borrowing well-labeled knowledge from the auxiliary source data. Recently, researchers have exploited multi-layer structures for discriminative feature learning to reduce the domain discrepancy. However, there are limited research efforts on simultaneously building a deep structure and a discriminative classifier over both labeled source and unlabeled target. In this paper, we propose a semi-supervised deep domain adaptation framework, in which the multi-layer feature extractor and a multi-class classifier are jointly learned to benefit from each other. Specifically, we develop a novel semi-supervised class-wise adaptation manner to fight off the conditional distribution mismatch between two domains by assigning a probabilistic label to each target sample, i.e., multiple class labels with different probabilities. Furthermore, a multi-class classifier is simultaneously trained on labeled source and unlabeled target samples in a semi-supervised fashion. In this way, the deep structure can formally alleviate the domain divergence and enhance the feature transferability. Experimental evaluations on several standard cross-domain benchmarks verify the superiority of our proposed approach.
Task-Driven Dictionary Learning for Hyperspectral Image Classification With Structured Sparsity Constraints

Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction error for the signal. However, in practice, this requirement is usually impaired by the lack of labeled training samples. Fortunately, previous research has shown that the requirement for a redundant dictionary can be less rigorous if simultaneous sparse approximation is employed, which can be carried out by enforcing various structured sparsity constraints on the sparse codes of the neighboring pixels. In addition, numerous works have shown that applying a variety of dictionary learning methods for the sparse representation model can also improve the classification performance. In this paper, we highlight the task-driven dictionary learning (TDDL) algorithm, which is a general framework for the supervised dictionary learning method. We propose to enforce structured sparsity priors on the TDDL method in order to improve the performance of the hyperspectral classification. Our approach is able to benefit from both the advantages of the simultaneous sparse representation and those of the supervised dictionary learning. We enforce two different structured sparsity priors, the joint and Laplacian sparsities, on the TDDL method and provide the details of the corresponding optimization algorithms. Experiments on numerous popular hyperspectral images demonstrate that the classification performance of our approach is superior to that of the sparse representation classifier with structured priors or the TDDL method.
Discriminative dictionary design using LVQ for hyperspectral image classification

In this paper, we propose a new technique for discriminative dictionary learning for hyperspectral image classification. The proposed algorithm generalizes the learning vector quantization scheme for sparse representation-based classifiers. It is known that a pixel can be represented by a sparse linear combination of atoms in a dictionary and its sparse representation vector contains the class information. The proposed learning technique utilizes the discriminative nature of the sparse vectors in the dictionary updating stage, generating a dictionary with both reconstructive and discriminative capabilities. Experimental results on a real hyperspectral data set demonstrate that using dictionaries learned from the proposed technique improves classification performance in various conditions.
Semisupervised Hyperspectral Classification Using Task-Driven Dictionary Learning With Laplacian Regularization

We present a semisupervised method for single-pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence probability of the predicted labels, for each unlabeled sample. Second, we propose to jointly optimize the classifier parameters and the dictionary atoms by a task-driven formulation, to ensure that the learned features (sparse codes) are optimal for the trained classifier. Finally, it incorporates spatial information through adding a Laplacian smoothness regularization to the output of the classifier, rather than the sparse codes, making the spatial constraint more flexible. The proposed method is compared with a few comparable methods for classification of several popular data sets, and it produces significantly better classification results.
Graph-based multi-sensor fusion for acoustic signal classification

Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e. rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as recently proposed joint sparsity models for multi-sensor acoustic signal classification.
Invariant object recognition based on a neural network of cascaded RCE nets

A neural network of cascaded restricted Coulomb energy (RCE) networks is constructed for object recognition. A number of RCE networks are cascaded together to form a classifier where the overlapping decision regions in a previously learned network are solved by the next network. The similarities among objects which have complex decision boundaries in the feature space are resolved by this multinetworks approach. The generalization ability of a RCE network recognition system, referring to the ability of the system to correctly recognize a new pattern even when the number of learning exemplars is small, is increased by the proposed coarse-to-fine learning strategy. A new feature extraction technique is proposed for mapping the geometrical shape information of an object into an ordered feature vector of fixed length which is the required form for input to this neural network
Invariant hierarchical sparse coding for object recognition via bags of atoms

In this paper, we introduce a novel local feature-based hierarchical framework to produce invariant sparse codes for object recognition. In order to enforce the invariant property for each sample patch (local feature descriptor) in the image, its sparse code is recovered with a dedicated dictionary whose atoms are adaptively chosen from several bags of candidate atoms. The single-layer invariant sparse coding model is naturally extended to a multi-layer hierarchical architecture to further improve the invariance of the sparse codes. Experimental results on the popular image datasets, including MNIST, CIFAR-10 and STL-10, verify the efficacy and robustness of the proposed algorithm.
A modular clutter rejection technique for FLIR imagery using region-based principal component analysis

In this paper, a modular clutter rejection technique using region-based principal component analysis (PCA) is proposed. Our modular clutter rejection system uses dynamic ROI (region of interest) extraction to overcome the problem of poorly centered targets. In dynamic ROI extraction, a representative ROI is moved in several directions with respect to the center of the potential target image to extract a number of ROIs. Each module in the proposed system applies region-based PCA to generate the feature vectors, which are subsequently used to decide about the identity of the potential target. We also present experimental results using real-life data evaluating and comparing the performance of the clutter rejection systems with static and dynamic ROI extraction.
Projection-based adaptive anomaly detection for hyperspectral imagery

Adaptive anomaly detectors that find any materials whose spectral characteristics are out of context with those of the neighboring materials are proposed. We use a dual rectangular window that separates the local area into two regions- the inner window region (IWR) and outer window region (OWR). The statistical differences between the IWR and OWR is exploited by generating projection vectors onto which the IWR and OWR vectors are projected. Anomalies are detected if the projection separation between the IWR and OWR vectors is greater than a predefined threshold. Four different methods are used to produce the projection vectors. The proposed anomaly detectors have been applied to HYDICE (HYper-spectral Digital Imagery Collection Experiment) images and detection performance for each method has been measured.
Multi-sensor classification via sparsity-based representation with low-rank interference

In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification which exploits correlation as well as complementary information among homogeneous and heterogeneous sensors while simultaneously extracting the low-rank interference term. Specifically, we observe that incorporating the noise or interfered signal as a low-rank component is essential in a multi-sensor 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 optimal solution is guaranteed. Extensive experiments are conducted on a real data set for a multi-sensor classification problem focusing on discriminating between human and animal footsteps. Results are compared with the conventional classifiers and existing sparsity-based representation methods to verify the effectiveness of our proposed models.
A modified Hopfield network approach for cellular radio channel assignment

A new channel assignment algorithm using a modified Hopfield 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 local minima is introduced. The interconnection weights between the neurons are designed in such a way that each neuron receives inhibitory support from neurons that violate the constraint conditions and receives excitatory support from external input. Various initialization techniques which use the specific characteristics of frequency assignment problems in cellular radio networks such as co-site constraint, adjacent channel constraint, and co-channel constraint and updating methods are investigated. Other than the already proposed neural network algorithms, this new algorithm fixes no frequencies of the cells before the frequency assignment procedure. This new algorithm, together with the proposed initialization and updating techniques and with no fixed frequencies in certain cells, has better performance results than the results reported previously utilizing fixed cells.
Subspace vertex pursuit for separable non-negative matrix factorization in hyperspectral unmixing

Recently, the separability assumption turns the nonnegative matrix factorization (NMF) into a tractable problem. The assumption coincides with the pixel purity assumption and provides new insights for the hyperspectral unmixing problem. In this paper, we present a quasi-greedy algorithm for solving the problem by employing a back-tracking strategy. Unlike the current greedy methods, the proposed method can refresh the endmember index set in every iteration. Therefore, our method has two important characteristics: (i) low computational complexity comparable to state-of-the-art greedy methods but (ii) empirically enhanced robustness against noise. Finally, computer simulations on synthetic hyperspectral data demonstrate the effectiveness of the proposed method.
An on-line cursive word recognition system

This paper presents a system for large vocabulary recognition of on-line handwritten cursive words. The system first uses a filtering module, based on simple letter features, to quickly reduce a large reference dictionary to a smaller number of candidates; the reduced lexicon along with the original input is subsequently fed to a recognition module. In order to exploit the sequential nature of the temporal data, we employ a TDNN-style network architecture which has been successfully used in the speech recognition domain. Explicit segmentation of the input words into characters is avoided by using a sliding window concept where the input word representation (a set of frames) is presented to the neural network-based recognizer sequentially. The outputs of the recognition module are collected and converted into a string of characters that can be matched with the candidate words. A description of the complete system and its components is given.<<ETX>>
Object-based SAR image compression using vector quantization

A simple and elegant algorithm is presented to encode images with rich content, which allows easy access to various objects. An object-plane-based encoding method for compression of synthetic aperture radar (SAR) imagery is developed, with different object planes for target classes and background. A variable-rate residual vector quantization (VQ) algorithm is developed to encode the background information. This algorithm is very powerful as indicated by the experimental results. The proposed coding scheme allows compression matched to the final application of the images, which in this case is target recognition and classification.
An Order Preserving Bilinear Model for Person Detection in Multi-Modal Data

We propose a new order preserving bilinear framework that exploits low-resolution video for person detection in a multi-modal setting using deep neural networks. In this setting cameras are strategically placed such that less robust sensors, e.g. geophones that monitor seismic activity, are located within the field of views (FOVs) of cameras. The primary challenge is being able to leverage sufficient information from videos where there are less than 40 pixels on targets, while also taking advantage of less discriminative information from other modalities, e.g. seismic. Unlike state-of-the-art methods, our bilinear framework retains spatio-temporal order when computing the vector outer products between pairs of features. Despite the high dimensionality of these outer products, we demonstrate that our order preserving bilinear framework yields better performance than recent orderless bilinear models and alternative fusion methods. Code is available at https://github.com/oulutan/OP-Bilinear-Model
Covariance trace for polarimetric anomaly detection

We propose a new method for autonomous manmade object detection, which is solely based on the use of second order statistics from two polarization components (0 and 90 deg) of polarimetric imagery. Using the approach, manmade objects can be detected as anomalies in scenes spatially dominated by natural objects. The approach exploits a key discovery: manmade objects are separable from natural objects in the (0 and 90 deg) variance-covariance space, holding invariant to diurnal cycle variation and geometry of illumination. Testing real imagery acquired outdoor (0.55 km sensor-to-target range) showed that the approach significantly outperforms the classical use of Stokes vector and DOLP (degree of linear polarization) during a full diurnal cycle.
Object recognition using multi-layer Hopfield neural network

An object recognition approach based on concurrent coarse-and-fine matching using a multi-layer Hopfield neural network is presented. The proposed network consists of several cascaded single layer Hopfield networks, each encoding object features at a distinct resolution, with bidirectional interconnections linking adjacent layers. The interconnection weights between nodes associating adjacent layers are structured to favor node pairs for which model translation and rotation, when viewed at the two corresponding resolutions, are consistent. This inter-layer feedback feature of the algorithm reinforces the usual intra-layer matching process in conventional single layer Hopfield nets in order to compute the model-object match which is most consistent across several resolution levels. The performance of the algorithm is demonstrated in cases of images containing single and multiple occluded objects. These results are compared with recognition results obtained using a single layer Hopfield network.<<ETX>>