nils-herrmann/clean_scirepeval huggingface dataset:
Design of Non-Linear Kernel Dictionaries for Object Recognition

In this paper, we present dictionary learning methods for sparse signal representations in a high dimensional feature space. Using the kernel method, we describe how the well known dictionary learning approaches, such as the method of optimal directions and KSVD, can be made nonlinear. We analyze their kernel constructions and demonstrate their effectiveness through several experiments on classification problems. It is shown that nonlinear dictionary learning approaches can provide significantly better performance compared with their linear counterparts and kernel principal component analysis, especially when the data is corrupted by different types of degradations.
Wiener prediction-based change detection for locating mines in multilook SAR imagery

In this paper, we present a Wiener-based change detection method and compare its performance with several other methods for a pair of multi-look synthetic aperture radar (SAR) images of the same scene. We implement and compare several techniques which vary in complexity. Among the simple methods that are implemented are differencing, Euclidean distance, and image ratioing. These methods require minimal processing time, with little computational complexity, and incorporate no statistical information. We also implemented methods which incorporate second order statistic calculations in making a change decision in efforts to mitigate false alarms arising from the speckle noise, misregistration errors, and nonlinear variations in SAR images. These methods include a Wiener prediction-based method, Mahalanobis distance measure and subspace projection method. We compare the performance of these methods using multi-look SAR images containing several targets (mines). We present results in the form of receiver operating characteristics (ROC) curves.
Hyperspectral Target Detection : An Overview of Current and Future Challenges

Over the last decade, hyperspectral imagery (HSI) obtained by remote sensing systems has provided significant information about the spectral characteristics of the materials in the scene. Typically, a hyperspectral spectrometer provides hundreds of narrow contiguous bands over a wide range of the electromagnetic spectrum. Hyperspectral sensors measure the reflective (or emissive) properties of objects in the visible and short-wave infrared (IR) regions (or the mid-wave and long-wave IR regions) of the spectrum. Processing of these data allows algorithms to detect and identify targets of interest in a hyperspectral scene by exploiting the spectral signatures of the materials , .
Subspace Vertex Pursuit: A Fast and Robust Near-Separable Nonnegative Matrix Factorization Method for Hyperspectral Unmixing

The separability assumption turns the nonnegative matrix factorization (NMF) problem tractable, which coincides with the pure pixel assumption and provides new insights for the hyperspectral unmixing problem. Based on this assumption, and starting from the data self-expressiveness perspective, we formulate the unmixing problem as a joint sparse recovery problem by using the data itself as a dictionary. Moreover, we present a quasi-greedy algorithm for this problem by employing a back-tracking strategy. In comparison with the previous greedy methods, the proposed method can refresh the candidate pixels by solving a small fixed-scale convex sub-problem in every iteration. Therefore, our method has two important characteristics: (i) enhanced robustness against noise; (ii) moderate computational complexity and scalability to large dataset. Finally, computer simulations on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.
Kernel task-driven dictionary learning for hyperspectral image classification

Dictionary learning algorithms have been successfully used in both reconstructive and discriminative tasks, where the input signal is represented by a linear combination of a few dictionary atoms. While these methods are usually developed under ℓ1 sparsity constrain (prior) in the input domain, recent studies have demonstrated the advantages of sparse representation using structured sparsity priors in the kernel domain. In this paper, we propose a supervised dictionary learning algorithm in the kernel domain for hyperspectral image classification. In the proposed formulation, the dictionary and classifier are obtained jointly for optimal classification performance. The supervised formulation is task-driven and provides learned features from the hyperspectral data that are well suited for the classification task. Moreover, the proposed algorithm uses a joint (ℓ12) sparsity prior to enforce collaboration among the neighboring pixels. The simulation results illustrate the efficiency of the proposed dictionary learning algorithm.
An admission control framework to support media-streaming over packet-switched networks

This paper presents an admission control framework to support 'streaming' of audio-video data over packet-switched networks. To ensure smooth functioning, the network provides a degree of assurance to the applications in the form of soft QOS guarantees. A measurement based admission control algorithm is developed to enable the network to provide the required service assurances. Sources are characterized using a statistical bounding model, which is fairly universal. A measurement procedure is first developed for online estimation of model parameters. This measurement procedure is then used in the admission control framework. Simulation results are presented and the performance of the algorithms are evaluated.
Large Vocabulary Recognition of On-Line Handwritten Cursive Words

This paper presents a writer independent 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 (lexicon) to a more manageable size; the reduced lexicon is subsequently fed to a recognition module. The recognition module uses a temporal representation of the input, instead of a static two-dimensional image, thereby preserving the sequential nature of the data and enabling the use of a Time-Delay Neural Network (TDNN); such networks have been previously successful in the continuous speech recognition domain. Explicit segmentation of the input words into characters is avoided by sequentially presenting the input word representation to the neural network-based recognizer. The outputs of the recognition module are collected and converted into a string of characters that is matched against the reduced lexicon using an extended Damerau-Levenshtein function. Trained on 2,443 unconstrained word images (11 k characters) from 55 writers and using a 21 k lexicon we reached a 97.9% and 82.4% top-5 word recognition rate on a writer-dependent and writer-independent test, respectively.
Segmentation based wavelet coding of digital images

In this paper, we present a segmentation based wavelet coding scheme, in which an image is segmented into two regions: stationary areas (background) and the areas containing edge information (foreground). These regions are then encoded independently using two dedicated encoders that are optimized for each region. A 2-D edge operator is used for segmenting the image. We use the embedded zerotree wavelet (EZW) algorithm for encoding the background due to its good performance on stationary areas. The foreground area is, however, encoded using a predictive residual vector quantizer (PRVQ). Experimental results show that the proposed technique improves the quality of the reconstructed images, both numerically (in terms of mean square error) and perceptually when compared to EZW at the same bit rate.
Neural network architectures for vector prediction

A vector predictor is an integral part of a predictive vector quantization coding scheme. The conventional techniques for designing a nonlinear predictor are extremely complex and suboptimal due to the absence of a suitable model for the source data. We investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the multilayer perceptron, the functional link network and the radial basis function network. We also evaluated and compared the performance of these neural network predictors with that of a linear vector predictor. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor. However, the performance of a neural network predictor is comparable to that of a linear predictor for predicting the stationary and shade blocks.
Multimodal Task-Driven Dictionary Learning for Image Classification

Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are mostly developed for single-modality scenarios, recent studies have demonstrated the advantages of feature-level fusion based on the joint sparse representation of the multimodal inputs. In this paper, we propose a multimodal task-driven dictionary learning algorithm under the joint sparsity constraint (prior) to enforce collaborations among multiple homogeneous/heterogeneous sources of information. In this task-driven formulation, the multimodal dictionaries are learned simultaneously with their corresponding classifiers. The resulting multimodal dictionaries can generate discriminative latent features (sparse codes) from the data that are optimized for a given task such as binary or multiclass classification. Moreover, we present an extension of the proposed formulation using a mixed joint and independent sparsity prior, which facilitates more flexible fusion of the modalities at feature level. The efficacy of the proposed algorithms for multimodal classification is illustrated on four different applications-multimodal face recognition, multi-view face recognition, multi-view action recognition, and multimodal biometric recognition. It is also shown that, compared with the counterpart reconstructive-based dictionary learning algorithms, the task-driven formulations are more computationally efficient in the sense that they can be equipped with more compact dictionaries and still achieve superior performance.
Discriminative and compact dictionary design for Hyperspectral Image classification using learning VQ framework

Sparse representation provides an efficient description for high-dimensional Hyperspectral Imagery (HSI) and also encodes discriminative information useful for classification. However, due to the large size of typical HSI images, the naive way to construct a dictionary with all training pixels is neither efficient nor practical. In this paper, a novel approach is proposed to design compact dictionary for Sparse Representation-based Classification (SRC). Inspired by Learning Vector Quantization (LVQ) techniques, we use a hinge loss function directly related to classification task as our objective function, and optimize the dictionary by exploiting the differentiable parts of sparse codes. The resultant dictionary updating procedure adapts the “push” and “pull” actions in LVQ to SRC, which is therefore named as Learning Sparse Representation-based Classification (LSRC). Experiments on different HSI images demonstrate that our LSRC approach can achieve higher classification accuracy with substantially smaller dictionary size than using the whole training set, and also outperforms existing dictionary learning methods.
Active Planning, Sensing, and Recognition Using a Resource-Constrained Discriminant POMDP

In this paper, we address the problem of object class recognition via observations from actively selected views/modalities/features under limited resource budgets. A Partially Observable Markov Decision Process (POMDP) is employed to find optimal sensing and recognition actions with the goal of long-term classification accuracy. Heterogeneous resource constraints -- such as motion, number of measurements and bandwidth -- are explicitly modeled in the state variable, and a prohibitively high penalty is used to prevent the violation of any resource constraint. To improve recognition performance, we further incorporate discriminative classification models with POMDP, and customize the reward function and observation model correspondingly. The proposed model is validated on several data sets for multi-view, multi-modal vehicle classification and multi-view face recognition, and demonstrates improvement in both recognition and resource management over greedy methods and previous POMDP formulations.
A committee of networks classifier with multi-resolution feature extraction for automatic target recognition

A neural network-based classifier has been applied to the problem of automatic target recognition (ATR) using forward-looking infrared (FLIR) imagery. The target classifier consists of several neural networks that form a committee for classification. Each neural network in the committee receives inputs from features extracted from only a local region of a target, known as a receptive field, and is trained independently from other committee members. The classification results of the individual neural networks are combined to determine the final classification. Our experiments show that this committee of networks classifier is superior to a fully connected neural network classifier in terms of complexity (number of weights to be learned) and performance (classification rate). The proposed classifier shows a high noise immunity to clutter or target obscuration due to the independence of the individual neural networks in the committee. Performance of the proposed classifier is further improved by the use of multi-resolution features and by the introduction of a higher level neural network on the top of committee, a method known as stacked generalization.
Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models

A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training.
Image compression using address-vector quantization

A novel vector quantization scheme, called the address-vector quantizer (A-VQ), is proposed. It is based on exploiting the interblock correlation by encoding a group of blocks together using an address-codebook. The address-codebook consists of a set of address-codevectors where each codevector represents a combination of addresses (indexes). Each element of this codevector is an address of an entry in the LBG-codebook, representing a vector quantized block. The address-codebook consists of two regions: one is the active (addressable) region, and the other is the inactive (nonaddressable) region. During the encoding process the codevectors in the address-codebook are reordered adaptively in order to bring the most probable address-codevectors into the active region. When encoding an address-codevector, the active region of the address-codebook is checked, and if such an address combination exist its index is transmitted to the receiver. Otherwise, the address of each block is transmitted individually. The quality (SNR value) of the images encoded by the proposed A-VQ method is the same as that of a memoryless vector quantizer, but the bit rate would be reduced by a factor of approximately two when compared to a memoryless vector quantizer.
Next-state functions for finite-state vector quantization

A finite-state vector quantizer called dynamic finite-state vector quantization (DFSVQ) is investigated with regard to its subcodebook construction. In DFSVQ each input vector is encoded by a small codebook called the subcodebook which is created from a much larger codebook called the supercodebook by selecting (reordering procedure) a set of appropriate codevectors. The performance of the DFSVQ depends on this reordering procedure. In the paper, several reordering procedures including the conditional histogram, address prediction, vector prediction, nearest neighbor design and the frequency usage of codevectors are introduced and their performance are evaluated by comparing their hit ratios (the number of blocks encoded by the subcodebook) and their computational complexity.<<ETX>>
A new vector quantization scheme using interblock correlation: address-vector quantizer

A vector quantization scheme called the address vector quantizer (A-VQ) is proposed. It is based on exploiting the interblock correlation to encode a group of blocks together by using an address-codebook. The address-codebook consists of a set of address code vectors each of which represents a combination of blocks, and each of its elements is the address of a LBG-codebook entry representing a vector-quantized block. The address-codebook consists of two regions: the active (addressable) region and the inactive (nonaddressable) region. During the encoding process the code vectors in the address-codebook are reordered adaptively in order to bring the most probable address code vectors into the active region. During the encoding of an address combination the active region of the address-codebook is checked. If such an address combination exists its index is transmitted to the receiver; otherwise the address of each block is transmitted individually. The technique was simulated on a digital computer and its performance was evaluated on several images outside the training sequence. The bit rate achieved was very close to the bit rate obtained from the entropy of the training data.<<ETX>>
Subband image coding using block-zero tree coding and vector quantization

The need for developing effective coding techniques for various multimedia services is increasing in order to meet the demand for image and video data. In this paper, a block-zero tree coding (BZTC) algorithm is proposed for multilayer coding and progressive transmission of images. The BZTC algorithm is constructed as an embedded coding so that the encoding and decoding process can be terminated at any point and allowing reasonable image quality. Some features of our BZTC algorithm are (1) prediction of the significance of blocks across scale, (2) state transition rules for representing the significance block map, and (3) block coding by vector quantization using a multiband codebook consisting of several subcodebooks dedicated for each band at a given threshold.
Improved target detector for FLIR imagery

Algorithms are considered for searching wide area forward-looking infrared imagery for military vehicles. Wide area search has typically been handled by using a simple detection algorithm with low computational cost to search the entire image or set of images, followed by a clutter rejection algorithm that analyzes only those portions of the image that are marked by the detection algorithm. We start with a feature based detector and a eigen-neural based clutter rejecter, and examine a number of architectures for combining these modules to maximize joint performance. The architectures considered include a clutter rejection threshold method and a nonlinear learning-based combination. The performance of the architectures are compared using a set of several thousand real images.
Interframe hierarchical vector quantization

An interframe coding scheme for the transmission of image sequences at bit rates below 0.2 bits per pixel is presented. The proposed scheme is called an interframe hierarchical (quadtree) vector quantizer. A regular decomposition quadtree method is used to segment the interframe differential signal into homogeneous regions of different block size. Small blocks representing high-contrast moving boundaries known as the impulsive component of the difference signal are vector-quantized. Large blocks typically representing smooth regions of the image are encoded by the local sample mean of the region. Experimental results are presented, confirming that the system can encode image sequence scenes at bit rates below 0.2 bit per pixel per frame. A comparison of the interframe hierarchical vector quantization system and an interframe mean reconstructed quadtree method is presented.<<ETX>>
Kernel subspace-based anomaly detection for hyperspectral imagery

This paper provides a performance comparison of various linear and nonlinear subspace-based anomaly detectors. Three different techniques, Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD) Analysis, and the Eigenspace Separation Transform (EST), are used to generate the linear projection subspaces. Each of these three linear methods is then extended to its corresponding nonlinear kernel version. The well-known Reed-Xiaoli (RX) anomaly detector and its kernel version (kernel RX) are also implemented. Comparisons between all linear and non-linear anomaly detectors are made using receiver operating characteristics (ROC) curves for several hyperspectral imagery.
Vector quantization of images based upon the Kohonen self-organizing feature maps

A neural-network clustering algorithm proposed by T. Kohonen (1986, 88) is used to design a codebook for the vector quantization of images. This neural-network clustering algorithm, which is better known as the Kohonen self-organizing feature maps, is a two-dimensional set of extensively interconnected nodes or unit of processors. The synaptic strengths between the input and the output nodes represent the centroid of the clusters after the network has been adapted to the input patterns. Input vectors are presented one at a time, and the weights connecting the input signals to the neurons are adaptively updated such that the point density function of the weights tends to approximate the probability density function of the input vector. Results are presented for a number of coded images using the codebook designed by the self-organization feature maps. The results are compared with coded images when the cookbook is designed by the Linde-Buzo-Gray algorithm.
Sparse coding with fast image alignment via large displacement optical flow

Sparse representation-based classifiers have shown outstanding accuracy and robustness in image classification tasks even with the presence of intense noise and occlusion. However, it has been discovered that the performance degrades significantly either when test image is not aligned with the dictionary atoms or the dictionary atoms themselves are not aligned with each other, in which cases the sparse linear representation assumption fails. In this paper, having both training and test images misaligned, we introduce a novel sparse coding framework that is able to efficiently adapt the dictionary atoms to the test image via large displacement optical flow. In the proposed algorithm, every dictionary atom is automatically aligned with the input image and the sparse code is then recovered using the adapted dictionary atoms. A corresponding supervised dictionary learning algorithm is also developed for the proposed framework. Experimental results on digit datasets recognition verify the efficacy and robustness of the proposed algorithm.