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.

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.

A superimposed (Super for short) minimum mean squared error (MMSE) predictive-transform (PT) residual vector quantization (RVQ) coder is introduced in this paper. The Super MMSE PT-RVQ coder arises as a straight forward generalization of a recently offered MMSE PT-RVQ coder and addresses in a simple and elegant fashion the noticeable blocking artifacts that are encountered with the MMSE PT-RVQ coder when operating at very low bit rates. The technique is illustrated by compressing five monochrome test images obtained from the JPEG suitcase at compression ratios as high as 229 to 1 or equivalently at bit rates as low as 0.035 bps. It is found that for all test images the Super PT-RVQ coder generally outperforms the perceptual quality and peak SNR derived with the Said and Pearlman wavelet algorithm.

This paper presents a new lossless image compression technique called modular differential pulse code modulation (MDPCM). The proposed technique consists of a VQ classifier and several neural network class predictors. The classifier uses the four previously encoded pixels to identify the class of the current pixel (the pixel to be predicted). The current pixel is then predicted by the corresponding class predictor. Experimental results demonstrate that the proposed technique reduces the bit rate by as much as 10 percent when compared to the lossless JPEG.

The preprocessing stage of an automatic target recognition system extracts areas containing potential targets from a battlefield scene. These potential target images are then sent to the classification stage to identify the targets. It is highly desirable at the preprocessing stage to minimize the incorrect rejection rate. This, however, results in a high false alarm rate. The high false alarm rate, in turn, makes subsequent target classification decisions unreliable. We present a new technique to reject false alarms (clutter images) produced by the preprocessing stage. Our technique, which we call region-based principal component analysis (PCA), uses topological features of the targets to reject false alarms. In this technique a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose to use regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target.

A novel modular coding paradigm is investigated using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks that are optimized for predicting a particular class of data. The predictor also consists of an integrating unit that mixes the outputs of the expert networks to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (modular PRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same peak signal-to-noise ratio (PSNR), the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.

Two key performance characterization of biometric algorithms (face recognition in particular) are (1) verification performance and (2) and performance as a function of database size and composition. This characterization is required for developing robust face recognition algorithms and for successfully transitioning algorithms from the laboratory to real world. In this paper we (1) present a general verification protocol and apply it to the results from the Sep96 FERET test, and (2) discuss and present results on the effects of database size and variability on identification and verification performance.

This work investigates a new compression scheme that incorporates rate-constrained residual vector quantization (RVQ) of the transform coefficient error vectors arising in a minimum mean squared error (MMSE) predictive-transform (PT) methodology. The proposed technique that we call predictive-transform residual vector quantization (PTRVQ) exploits the correlation between neighboring blocks by predicting the transform coefficients of each block using information from previously encoded blocks. PTRVQ, therefore, exploits both the intra- and inter-block correlation as well as employs dimensionality reduction in the transform domain in order to generate a highly compact code. Experimental results obtained with several standard monochrome images and compression ratios as high as 135:1 demonstrate quite noticeable improvements of the reconstructed images in both perceptual quality and peak signal to noise ratio (PSNR). The PSNR improvements are as high as 1.4 dB over a wavelet based coder.

In this paper, we present a modular neural network vector predictor that improves the predictive component of a predictive vector quantization (PVQ) scheme. The proposed vector prediction technique consists of five dedicated predictors (experts), where each expert predictor is optimized for a particular class of input vectors. An input vector is classified into one of five classes, based on its directional variances. One expert predictor is optimized for stationary blocks, and each of the other four expert predictors are optimized to predict horizontal, vertical, 45 degrees , and 135 degrees diagonally oriented edge-blocks, respectively. An integrating unit is then used to select or combine the outputs of the experts in order to form the final output of the modular network. Therefore, no side information is transmitted to the receiver about the selected predictor or the integration of the predictors. Experimental results show that the proposed scheme gives an improvement of 1.7 dB over a single multilayer perceptron (MLP) predictor. Furthermore, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over a single MLP predictor. The perceptual quality of the predicted images is also significantly improved.

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1199 individuals are included in the FERET database, which is divided into development and sequestered portions. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to (1) assess the state of the art, (2) identify future areas of research, and (3) measure algorithm performance on large databases.

This paper presents a predictive residual vector quantization (PRVQ) scheme using a modular neural network vector predictor. The proposed PRVQ scheme takes the advantage of the high prediction gain and the improved edge fidelity of a modular neural network vector predictor in order to implement a high performance vector quantization (VQ) scheme with low search complexity and a high perceptual quality. Simulation results show that the proposed PRVQ with modular vector predictor outperforms the equivalent PRVQ with general vector predictor (operating at the same bit rate) by more than 1 dB. Furthermore, the perceptual quality of the reconstructed image is also improved.

This paper investigates a novel modular image coding paradigm using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks, where each expert network is optimized for predicting a particular class of data, and an integrating unit that mixes the outputs of the expert networks in order to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (MPRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same PSNR, the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.

Finite-state vector quantization (FSVQ) is known to give better performance than the memoryless vector quantization (VQ). This paper presents a new FSVQ scheme, called finite-state residual vector quantization (FSRVQ), in which each state uses a residual vector quantizer (RVQ) to encode the input vector. This scheme differs from the conventional FSVQ in that the state-RVQ codebooks encode the residual vectors instead of the original vectors. A neural network predictor estimates the current block based on the four previously encoded blocks. The predicted vector is then used to identify the current state as well as to generate a residual vector (the difference between the current vector and the predicted vector). This residual vector is encoded using the current state-RVQ codebooks. A major task in designing our proposed FSRVQ is the joint optimization of the next-state codebook and the state-RVQ codebooks. This is achieved by introducing a novel tree-structured competitive neural network in which the first layer implements the next-state function, and each branch of the tree implements the corresponding state-RVQ. A joint training algorithm is also developed that mutually optimizes the next-state and the state-RVQ codebooks for the proposed FSBVQ. Joint optimization of the next-state function and the state-RVQ codebooks eliminates a large number of redundant states in the conventional FSVQ design; consequently, the memory requirements are substantially reduced in the proposed FSRVQ scheme. The proposed FSRVQ can be designed for high bit rates due to its very low memory requirements and the low search complexity of the state-RVQ's. Simulation results show that the proposed FSRVQ scheme outperforms conventional FSVQ schemes both in terms of memory requirements and the visual quality of the reconstructed image. The proposed FSRVQ scheme also outperforms JPEG (the current standard for still image compression) at low bit rates.

The performance of a classical linear vector predictor is limited by its ability to exploit only the linear correlation between the blocks. However, a nonlinear predictor exploits the higher order correlations among the neighboring blocks, and can predict edge blocks with increased accuracy. We have investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the multilayer perceptron (MLP), the functional link (FL) network, and the radial basis function (RBF) network. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor.

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.

In this paper, we present a modular neural network vector predictor in order to improve the predictive component of a predictive vector quantization (PVQ) scheme. The proposed vector prediction technique consists of five dedicated predictors (experts), where each expert predictor is optimized for a particular class of input vectors. An input vector is classified into one of five classes based on its directional variances. One expert is optimized for stationary blocks, and each of the other four experts are optimized to predict horizontal, vertical, 45/spl deg/, and 135/spl deg/ diagonally oriented edge-blocks, respectively. An integrating unit is then used to select or combine the outputs of the experts in order to form the final output of the modular network. Therefore, no side information is required to transmit to the receiver about the predictor selection. Experimental results show that the proposed scheme gives an improvement of 1 to 1.5 dB better than a single multilayer perceptron (MLP) predictor. However, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over the single MLP predictor. The perceptual quality of the predicted images is also significantly improved.

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.

Advances in residual vector quantization (RVQ) are surveyed. Definitions of joint encoder optimality and joint decoder optimality are discussed. Design techniques for RVQs with large numbers of stages and generally different encoder and decoder codebooks are elaborated and extended. Fixed-rate RVQs, and variable-rate RVQs that employ entropy coding are examined. Predictive and finite state RVQs designed and integrated into neural-network based source coding structures are revisited. Successive approximation RVQs that achieve embedded and refinable coding are reviewed. A new type of successive approximation RVQ that varies the instantaneous block rate by using different numbers of stages on different blocks is introduced and applied to image waveforms, and a scalar version of the new residual quantizer is applied to image subbands in an embedded wavelet transform coding system.

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.

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. IEEE Int. Conf. Image Processing (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.

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.

A major problem with a VQ-based image compression scheme is its codebook search complexity. Recently, a predictive residual vector quantizer (PRVQ) was proposed by Rizvi and Nasrabadi (see IEEE Int. Conf. Image Processing, Austin, vol.1, p.608-612, Nov. 13-16, 1994). This scheme has a very low search complexity, and its performance is very close to that of the predictive vector quantizer (PVQ). The article presents a new VQ scheme called variable-rate PRVQ (VR-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. The proposed VR-PRVQ is found to give an excellent rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by the Joint Photographic Experts Group (JPEG).>

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.>

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.

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.>

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.>

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.

This paper presents a new technique for designing a jointly optimized residual vector quantizer (RVQ). In conventional stage-by-stage design procedure, each stage codebook is optimized for that particular stage distortion and does not consider the distortion from the subsequent stages. However, the overall performance can be improved if each stage codebook is optimized by minimizing the distortion from the subsequent stage quantizers as well as the distortion from the previous stage quantizers. This can only be achieved when stage codebooks are jointly designed for each other. In this paper, the proposed codebook design procedure is based on a multilayer competitive neural network where each layer of this network represents one stage of the RVQ. The weight connecting these layers form the corresponding stage codebooks of the RVQ. The joint design problem of the RVQ's codebooks (weights of the multilayer competitive neural network) is formulated as a nonlinearly constrained optimization task which is based on a Lagrangian error function. This Lagrangian error function includes all the constraints that are imposed by the joint optimization of the codebooks. The proposed procedure seeks a locally optimal solution by iteratively solving the equations for this Lagrangian error function. Simulation results show an improvement in the performance of an RVQ when designed using the proposed joint optimization technique as compared to the stage-by-stage design, where both generalized Lloyd algorithm (GLA) and the Kohonen learning algorithm (KLA) were used to design each stage codebook independently, as well as the conventional joint-optimization technique.>

A joint optimization technique is developed for designing the predictor and quantizer of a predictive vector quantizer (PVQ). The proposed technique is based on a constrained optimization technique that makes use of a Lagrangian formulation and iteratively solves the Lagrangian error function to obtain a locally optimal solution for the predictor and quantizer. Simulation results show that the proposed PVQ design outperforms the conventional PVQ schemes, such as the closed-loop design and the jointly-optimized technique.>

In this paper, 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 encoded by a small codebook, called subcodebook, is created from a much larger codebook called supercodebook. The subcodebook is constructed by selecting (reordering procedure) a set of appropriate codevectors from the supercodebook. The performance of the DFSVQ depends on this reordering procedure, therefore, several reordering procedures are introduced and their performances are evaluated in this paper. The reordering procedures that are investigated are the conditional histogram, address prediction, vector prediction, nearest neighbor design, and the frequency usage of codevectors. The performance of the reordering procedures are evaluated by comparing their hit ratios (the number of blocks encoded by the subcodebook) and their computational complexity. Experimental results are presented for both still images and video. It is found that for still images the conditional histogram performs the best and for video the nearest neighbor design performs the best.