An adaptable connectionist text-retrieval system with relevance feedback.

This paper introduces a new connectionist network for certain domain-specific text-retrieval and search applications with expert end users. A new model reference adaptive system is proposed that involves three learning phases. Initial model-reference learning is first performed based upon an ensemble set of input-output of an initial reference model. Model-reference following is needed in dynamic environments where documents are added, deleted, or updated. Relevance feedback learning from multiple expert users then optimally maps the original query using either a score-based or a click-through selection process. The learning can be implemented, in regression or classification modes, using a three-layer network. The first layer is an adaptable layer that performs mapping from query domain to document space. The second and third layers perform document-to-term mapping, search/retrieval, and scoring tasks. The learning algorithms are thoroughly tested on a domain-specific text database that encompasses a wide range of Hewlett Packard (HP) products and for a large number of most commonly used single- and multiterm queries.

Underwater target classification in changing environments using an adaptive feature mapping.

A new adaptive underwater target classification system to cope with environmental changes in acoustic backscattered data from targets and nontargets is introduced. The core of the system is the adaptive feature mapping that minimizes the classification error rate of the classifier. The goal is to map the feature vector in such a way that the mapped version remains invariant to the environmental changes. A K-nearest neighbor (K-NN) system is used as a memory to provide the closest matches of an unknown pattern in the feature space. The classification decision is done by a backpropagation neural network (BPNN). Two different cost functions for adaptation are defined. These two cost functions are then combined together to improve the classification performance. The test results on a 40-kHz linear FM acoustic backscattered data set collected from six different objects are presented. These results demonstrate the effectiveness of the adaptive system versus nonadaptive system when the signal-to-reverberation ratio (SRR) in the environment is varying.

Comparison of two different PNN training approaches for satellite cloud data classification.

Presents a training algorithm for probabilistic neural networks (PNN) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data.

Temporal updating scheme for probabilistic neural network with application to satellite cloud classification--further results.

A novel temporal updating approach for probabilistic neural network classifiers was developed by Tian et al. (2000) to account for temporal changes of spectral and temperature features of clouds in the visible and infrared GOES 8 (Geostationary Operational Environmental Satellite) imagery data. In this paper, a new method referred to as moving singular value decomposition (MSVD) is introduced to improve the classification rate of the boundary blocks or blocks containing cloud types with non-uniform texture. The MSVD method is then incorporated into the temporal updating scheme and its effectiveness is demonstrated on several sequences of GOES 8 cloud imagery data. These results indicate that the incorporation of the new MSVD improves the overall performance of the temporal updating process by almost 10%

Underwater target classification using wavelet packets and neural networks.

In this paper, a new subband-based classification scheme is developed for classifying underwater mines and mine-like targets from the acoustic backscattered signals. The system consists of a feature extractor using wavelet packets in conjunction with linear predictive coding (LPC), a feature selection scheme, and a backpropagation neural-network classifier. The data set used for this study consists of the backscattered signals from six different objects: two mine-like targets and four nontargets for several aspect angles. Simulation results on ten different noisy realizations and for signal-to-noise ratio (SNR) of 12 dB are presented. The receiver operating characteristic (ROC) curve of the classifier generated based on these results demonstrated excellent classification performance of the system. The generalization ability of the trained network was demonstrated by computing the error and classification rate statistics on a large data set. A multiaspect fusion scheme was also adopted in order to further improve the classification performance.

Temporal updating scheme for probabilistic neural network with application to satellite cloud classification.

In cloud classification from satellite imagery, temporal change in the images is one of the main factors that causes degradation in the classifier performance. In this paper, a novel temporal updating approach is developed for probabilistic neural network (PNN) classifiers that can be used to track temporal changes in a sequence of images. This is done by utilizing the temporal contextual information and adjusting the PNN to adapt to such changes. Whenever a new set of images arrives, an initial classification is first performed using the PNN updated up to the last frame while at the same time, a prediction using Markov chain models is also made based on the classification results of the previous frame. The results of both the old PNN and the predictor are then compared. Depending on the outcome, either a supervised or an unsupervised updating scheme is used to update the PNN classifier. Maximum likelihood (ML) criterion is adopted in both the training and updating schemes. The proposed scheme is examined on both a simulated data set and the Geostationary Operational Environmental Satellite (GOES) 8 satellite cloud imagery data. These results indicate the improvements in the classification accuracy when the proposed scheme is used.

A least-squares-based 2-D filtering scheme for stereo image compression.

A two-dimensional (2-D) least squares (LS)-based filtering scheme for high fidelity stereo image compression applications is introduced in this correspondence. This method removes the effects of mismatching in a stereo image pair by applying the left image as the reference input to a 2-D transversal filter while the right image is used as the desired output. The weights of the filter are computed using a block-based LS method. A reduced order filtering scheme is also introduced to find the optimum number of filter coefficients. The principal coefficients and the disparity vectors are used together with left image to reconstruct the right image at the receiver, The proposed schemes are examined on a real stereo image pair for 3D-TV applications and the results were benchmarked against those of the block-matching method.

A study of cloud classification with neural networks using spectral and textural features.

The problem of cloud data classification from satellite imagery using neural networks is considered in this paper. Several image transformations such as singular value decomposition (SVD) and wavelet packet (WP) were used to extract the salient spectral and textural features attributed to satellite cloud data in both visible and infrared (IR) channels. In addition, the well-known gray-level cooccurrence matrix (GLCM) method and spectral features were examined for the sake of comparison. Two different neural-network paradigms namely probability neural network (PNN) and unsupervised Kohonen self-organized feature map (SOM) were examined and their performance were also benchmarked on the geostationary operational environmental satellite (GOES) 8 data. Additionally, a postprocessing scheme was developed which utilizes the contextual information in the satellite images to improve the final classification accuracy. Overall, the performance of the PNN when used in conjunction with these feature extraction and postprocessing schemes showed the potential of this neural-network-based cloud classification system.

Neural network decision directed edge-adaptive Kalman filter for image estimation.

A neural network-based scheme for decision directed edge-adaptive Kalman filtering is introduced in this work. A backpropagation neural network makes the decisions about the orientation of the edges based on the information in a window centered at the current pixel being processed. Then based upon the neural network output an appropriate image model which closely matches the local statistics of the image is chosen for the Kalman filter. This prevents the oversmoothing of the edges, which would have otherwise been caused by the standard Kalman filter. Simulation results are presented which show the effectiveness of the proposed scheme.

Detection of mines and minelike targets using principal component and neural-network methods.

This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signal-to-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.

Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme.

This paper is concerned with the problem of cancellation of heart sounds from the acquired respiratory sounds using a new joint time-delay and signal-estimation (JTDSE) procedure. Multiresolution discrete wavelet transform (DWT) is first applied to decompose the signals into several subbands. To accurately separate the heart sounds from the acquired respiratory sounds, time-delay estimation (TDE) is performed iteratively in each subband using two adaptation mechanisms that minimize the sum of squared errors between these signals. The time delay is updated using a nonlinear adaptation, namely the Levenberg-Marquardt (LM) algorithm, while the function of the other adaptive system-which uses the block fast transversal filter (BFTF)-is to minimize the mean squared error between the outputs of the delay estimator and the adaptive filter. The proposed methodology possesses a number of key benefits such as the incorporation of multiple complementary information at different subbands, robustness in presence of noise, and accuracy in TDE. The scheme is applied to several cases of simulated and actual respiratory sounds under different conditions and the results are compared with those of the standard adaptive filtering. The results showed the promise of the scheme for the TDE and subsequent interference cancellation.

Reduced order Kalman filtering for the enhancement of respiratory sounds.

In the processing and analysis of respiratory sounds, heart sounds present the main source of interference. This paper is concerned with the problem of cancellation of the heart sounds using a reduced-order Kalman filter (ROKF). To facilitate the estimation of the respiratory sounds, an autoregressive (AR) model is fitted to heart signal information present in the segments of the acquired signal which are free of respiratory sounds. The state-space equations necessary for the ROKF are then established considering the respiratory sound as a colored additive process in the observation equation. This scheme does not require a time alignment procedure as with the adaptive filtering-based schemes. The scheme is applied to several synthesized signals with different signal-to-interference ratios (SIR) and the results are presented.

A fast learning algorithm for Gabor transformation.

An adaptive learning approach for the computation of the coefficients of the generalized nonorthogonal 2-D Gabor (1946) transform representation is introduced. The algorithm uses a recursive least squares (RLS) type algorithm. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. The proposed RLS learning offers better accuracy and faster convergence behavior when compared with the least mean squares (LMS)-based algorithms. Applications of this scheme in image data reduction are also demonstrated.

Principal component extraction using recursive least squares learning.

A new neural network-based approach is introduced for recursive computation of the principal components of a stationary vector stochastic process. The neurons of a single-layer network are sequentially trained using a recursive least squares squares (RLS) type algorithm to extract the principal components of the input process. The optimality criterion is based on retaining the maximum information contained in the input sequence so as to be able to reconstruct the network inputs from the corresponding outputs with minimum mean squared error. The proof of the convergence of the weight vectors to the principal eigenvectors is also established. A simulation example is given to show the accuracy and speed advantages of this algorithm in comparison with the existing methods. Finally, the application of this learning algorithm to image data reduction and filtering of images degraded by additive and/or multiplicative noise is considered.

Recursive dynamic node creation in multilayer neural networks.

The derivations of a novel approach for simultaneous recursive weight adaptation and node creation in multilayer backpropagation neural networks are presented. The method uses time and order update formulations in the orthogonal projection method to derive a recursive weight updating procedure for the training process of the neural network and a recursive node creation algorithm for weight adjustment of a layer with added nodes during the training process. The proposed approach allows optimal dynamic node creation in the sense that the mean-squared error is minimized for each new topology. The effectiveness of the algorithm is demonstrated on several benchmark problems (the multiplexer and the decoder problems) as well as a real world application for detection and classification of buried dielectric anomalies using a microwave sensor.

A full-plane block Kalman filter for image restoration.

A two-dimensional method which uses a full-plane image model to generate a more accurate filtered estimate of an image that has been corrupted by additive noise and full-plane blur is presented. Causality is maintained within the filtering process by using multiple concurrent block estimators. In addition, true state dynamics are preserved, resulting in an accurate Kalman gain matrix. Simulation results on a test image corrupted by additive white Gaussian noise are presented for various image models and compared to those of the previous block Kalman filtering methods.