LBG Algorithm Research Articles

Human Activity Recognition from Environmental Background Sounds for Wireless Sensor Networks

Sound feature extraction Mel Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) classification Linde-Buzo-Gray algorithm (LBG) algorithms are applied for recognizing the background sounds in the human daily activities. Applying these algorithms to twenty typical daily activity sounds, average recognition accuracy of 93.8% can be achieved. In these algorithms, how three parameters (i.e., Mel filters number, frame-to-frame overlap and LBG codebook cluster number) affect system's calculation burden and accuracy is also investigated. By adjusting these three parameters to an optimized combination, the multiplication and addition calculation burden can be reduced by 87.0% and 87.1% individually while maintaining the system's average accuracy rate at 92.5%. This is promising for future integration with other sensor (s) to fulfill daily activity recognition by using power aware Wireless Sensor Networks (WSN) systems.

Image Segmentation of Mammographic Images Using Kekre’S Proportionate Error Technique on Probability Images

Mammography is well known method for detection of breast tumors. Early detection and removal of the primary tumor is an essential and effective method to enhance survival rate and reduce mortality. In this paper, proposed algorithm uses probability of mammographic image as input for vector quantization .For region forming Kekre's Proportionate Error (KPE) algorithm is used and codebook of size 128 is formed .Further this 128 clusters were used for region merging using KPE algorithm for reclustering .To separate tumor ,post processing is done by morphological operations. For this tumor sectional area is calculated and center point is compared with LBG algorithm for segmentation of mammographic images.

Fixed Representative Colors Feature Extraction Algorithm for Moving Picture Experts Group-7 Dominant Color Descriptor

Problem statement: MPEG-7 has defined a set of color descriptors for the multimedia contents; one of these descriptors is the Dominant Color Descriptor (DCD). The DCD adopted the Generalized Lloyd Algorithm (GLA) as a feature extraction technique. However, using this algorithm in image retrieval systems may produce perceptually irrelevant images in the ranked results. Approach: In this study, we proposed a new technique for the extraction of the DCD called Fixed Representative Colors Feature Extraction Algorithm (FRCFE). This technique is based on converting the original image colors to a set of predefined co lors in the RGB color space. Also, we developed an image retrieval system to measure the effectives of this technique. Results: Experimental results revealed that FRCFE provides better accurate and perceptually relevant image retrieval for the MPEG- 7 DCD than GLA in the chosen dataset. Conclusion: Mapping the original image colors to a set of predefined colors preserved the main features of th e image and can produce more accurate results when used in image retrieval systems.

Optimizing Pilot Locations Using Feedback in OFDM Systems

Pilot-aided orthogonal frequency-division multiplexing (OFDM) channel estimation has, so far, been optimized only for open-loop systems for which the uniform spacing of pilots is optimal. In this paper, we examine closed-loop OFDM systems and show how uniform pilots may no longer be optimal. In doing so, we propose a feedback technique in which the pilot allocation mechanism is adapted to the mobile channel. After deriving a general expression for the symbol error rate as a function of pilot allocation, we optimize the pilot locations and propose two practical solutions using bounds on the objective function. We proceed to formulate pilot allocation based on the maximal average OFDM channel capacity. We examine the feedback overhead that is associated with our method and make comparisons with the conventional uniform framework. Finally, we suggest the use of vector quantization in the context of the generalized Lloyd algorithm to reduce feedback and extend our method to a multiple-input-multiple-output (MIMO)-OFDM system based on the Alamouti space-time block code. Monte-Carlo simulations illustrate the error-rate/capacity performance gains via nonuniform pilots and illustrate the effects of Doppler frequency and carrier frequency offset.

Bit-Interleaved Coded Multiple Beamforming with Imperfect CSIT

This paper addresses the performance of bit-interleaved coded multiple beamforming (BICMB) [1], [2] with imperfect knowledge of beamforming vectors. Most studies for limited-rate channel state information at the transmitter (CSIT) assume that the precoding matrix has an invariance property under an arbitrary unitary transform. In BICMB, this property does not hold. On the other hand, the optimum precoder and detector for BICMB are invariant under a diagonal unitary transform. In order to design a limited-rate CSIT system for BICMB, we propose a new distortion measure optimum under this invariance. Based on this new distortion measure, we introduce a new set of centroids and employ the generalized Lloyd algorithm for codebook design. We provide simulation results demonstrating the performance improvement achieved with the proposed distortion measure and the codebook design for various receivers with linear detectors.We show that although these receivers have the same performance for perfect CSIT, their performance varies under imperfect CSIT.

Fuzzy declustering-based vector quantization

Vector quantization is a useful approach for multi-dimensional data compression and pattern classification. One of the most popular techniques for vector quantization design is the LBG (Linde, Buzo, Gray) algorithm. To address the problem of producing poor estimate of vector centroids which are subjected to biased data in vector quantization; we propose a fuzzy declustering strategy for the LBG algorithm. The proposed technique calculates appropriate declustering weights to adjust the global data distribution. Using the result of fuzzy declustering-based vector quantization design, we incorporate the notion of fuzzy partition entropy into the distortion measures that can be useful for classification of spectral features. Experimental results obtained from simulated and real data sets demonstrate the effective performance of the proposed approach.

Expansive competitive learning for kernel vector quantization

In this paper we present a necessary and sufficient condition for global optimality of unsupervised Learning Vector Quantization (LVQ) in kernel space. In particular, we generalize the results presented for expansive and competitive learning for vector quantization in Euclidean space, to the general case of a kernel-based distance metric. Based on this result, we present a novel kernel LVQ algorithm with an update rule consisting of two terms: the former regulates the force of attraction between the synaptic weight vectors and the inputs; the latter, regulates the repulsion between the weights and the center of gravity of the dataset. We show how this algorithm pursues global optimality of the quantization error by means of the repulsion mechanism. Simulation results are provided to show the performance of the model on common image quantization tasks: in particular, the algorithm is shown to have a superior performance with respect to recently published quantization models such as Enhanced LBG [Patané, G., Russo, M., 2001. The enhanced LBG algorithm. Neural Networks 14 (9), 1219–1237] and Adaptive Incremental LBG [Shen, F., Hasegawa, O., 2006. An adaptive incremental LBG for vector quantization. Neural Networks 19 (5), 694–704].

Beamforming with limited feedback in amplify-and-forward cooperative networks - [transactions letters

A relay selection approach has previously been shown to outperform repetition-based scheduling for both amplify-and-forward (AF) and decode-and-forward (DF) cooperative networks. The selection method generally requires some feedback from the destination to the relays and the source, raising the issue of the interplay between performance and feedback rate. In this letter, we treat selection as an instance of limited feedback distributed beamforming in cooperative AF networks, and highlight the differences between transmit beamforming in a traditional multi-input single-output (MISO) system and the distributed case. Specifically, Grassmannian line packing (GLP) is no longer the optimal codebook design, and orthogonal codebooks are no longer equivalent to each other. We derive the high signal-to-noise ratio expressions for outage probability and probability of symbol error for unlimited-feedback and selection schemes, which are then used for performance comparisons. The selection protocol is compared to a limited-feedback distributed beamformer that assigns codebooks based on the Generalized Lloyd algorithm (GLA), and one that uses random beam-vectors. The main conclusion is that the performance improvement to be seen using the very complex GLA is small, and that many more feedback bits are required with random beamforming than selection for the same performance. These results indicate that the selection protocol is a very attractive protocol, with low complexity, that provides excellent performance relative to other known methods.

Redesigning vector quantizers using implicitly estimated probability density functions

Vector quantization is the most general form of quantization, and is a critical step in many compression systems for reducing the bitrate while controlling the distortion in the reconstructed signal. A vector quantizer (VQ) is usually designed using the generalized Lloyd algorithm which requires a training set that is assumed to be drawn from some underlying pdf characterizing the signal to be compressed. We consider here the problem of redesigning an existing VQ for some transformed or repartitioned signal space. Since VQ design is similar to the construction of nonlinear bin histograms, the VQ codebook provides an estimate of the pdf underlying the training set that was originally used to design it. We exploit this observation to synthesize training sets from the VQ codebooks such that they have statistics similar to those of the unknown original training sets. Using the proposed training set synthesis approach, we achieve improvements in performance of between 9.5% and 34% in VQ partitioning and redesign applications compared to a directly repartitioning and transforming the given VQ codebooks.

Multimode Precoding for MIMO Systems: Performance Bounds and Limited Feedback Codebook Design

This correspondence investigates the problem of designing the precoding codebook for limited feedback multiple-input multiple-output (MIMO) systems. We first analyze the asymptotic capacity loss of a suboptimal multimode precoding scheme as compared to optimal waterfilling and show that the suboptimal scheme is sufficient when negligible capacity loss is allowed. This knowledge is then applied to the design of the limited feedback codebook. In the design, the generalized Lloyd algorithm is employed, where the computation of the centroid is formulated as an optimization problem and solved optimally. Numerical results show that the proposed codebook design outperforms the comparable algorithms reported in the literature.

Distributed beamforming in wireless relay networks with quantized feedback

This paper is on quantized beamforming in wireless amplify-and-forward (AF) relay networks. We use the generalized Lloyd algorithm (GLA) to design the quantizer of the feedback information and specifically to optimize the bit error rate (BER) performance of the system. Achievable bounds for different performance measures are derived. First, we analytically show that a simple feedback scheme based on relay selection can achieve full diversity. Unlike the previous diversity analysis on the relay selection scheme, our analysis is not aided by any approximations or modified forwarding schemes. Then, for highrate feedback, we find an upper bound on the average signalto- noise ratio (SNR) loss. Using this result, we demonstrate that both the average SNR loss and the capacity loss decay at least exponentially with the number of feedback bits. In addition, we provide approximate upper and lower bounds on the BER, which can be calculated numerically.We observe that our designs can achieve both full diversity as well as high array gain with only a moderate number of feedback bits. Simulations also show that our approximate BER is a reliable estimation on the actual BER. We also generalize our analytical results to asynchronous networks, where perfect carrier level synchronization is not available among the relays.

Detection and classification of the spliced yarn joint using vector quantization and dynamic time warping

This paper presents an automatic vision based system for unsupervised detection and classification of spliced yarn joint. In the splice detection process, a competitive learning method based on LBG algorithm is used. In the splice classification process, a dynamic time warping (DTW) algorithm is used to classify the extracted splice joint into one of three degrees of quality based on the degree of similarity between the spliced joint and the non-spliced part of the same yarn. The use of DTW in the classification makes the proposed method adaptable to different types of yarns. Consequently, this method might be globally optimal for classification of all spliced yarn joints. The proposed method has been evaluated using three sorts of experiments. One hundred and twenty images containing different grades of joint quality have been used in the first experiments, where 97.5% detection rate and 95.8% classification accuracy were achieved. In the second experiment, 280 standard images that represent high quality reference images of 12 groups of spliced yarns are used. In this experiment 97.1% detection rate and 100% classification rate have been achieved. Moreover, the method has been tested using the Murata Image Database; this is a large set of high quality images of splice joints for different yarn types.

A G 2LA vector quantization for image data coding

In this paper, based on the Grey theory, a novel measurement method in a large volume and high dimension of information system is proposed for vector quantization (VQ) design and applied to image data coding. In the VQ coding procedure, it is often needs several epochs of clustering and always fails to obtain a better codebook; for instance, the well-known generalized Lloyd algorithm (GLA) easily traps into suboptimal codebook and does not have the ability to locate an optimal codebook during any clustering iteration with a random initial codebook. Hence, we propose a G 2LA design to solve heavy times of clustering procedure and at least to gain the best suboptimal codebook. In order to avoid edge degradation, firstly, the new selection of initial codevectors is adopted as the fast grey vector quantization (FGVQ) procedure which chooses nonhomogeneous vectors from a large volume image data. Then extending the GLA to G 2LA method by utilizing the measurement of grey relational analysis (GRA) which depends on the effect of relative objective and initial codevectors of FGVQ to obtain a better representative codebook. Experiment results show that at the same bit rate the G 2LA has not only the quickly convergence time but also high quality reconstructed image than traditional GLA technique with Euclidean distance measure, especially in high dimension and a large volume data system.

HEURISTIC PARTICLE SWARM OPTIMIZATION LEARNING-BASED IMAGE COMPRESSION SYSTEM

Traditional LBG algorithm is a pure iterative optimization procedure to achieve the vector quantization (VQ) codebook, where an initial codebook is continually refined at every iteration to reduce the distortion between code-vectors and a given training data set. However, such interactive type learning algorithms will easily direct final results converging toward the local optimization while the high quality of the initial codebook is not available. In this article, an efficient heuristic-based learning method, called novel particle swarm optimization (NPSO), is proposed to design the proper codebook of VQ scheme that can develop the image compression system. To improve the performance of the basic PSO, the centroid updating machine applies the one step-size gradient descent learning step in the heuristic learning procedure. Additionally, the presented NPSO with advantages of the centroid updating machine is proposed to quickly achieve the near-optimal reconstructive image. For demonstrating the proposed NPSO learning scheme, the image with several horizontal grey bars is first applied to present the efficiency of the NPSO learning mechanism. LBG and NPSO learning methods are also applied to test the reconstructing performance in several type images “Lena,” “Airplane,” “Cameraman”, and “peppers.” In our experiments, the NPSO learning algorithm provides the higher performance than conventional LBG methods in the application of building image compression system.

Vector quantization of images with variable block size

We proposed a vector quantization (VQ) with variable block size using local fractal dimensions (LFDs) of an image. A VQ with variable block size has so far been implemented using a quad tree (QT) decomposition algorithm. QT decomposition carries out image partitioning based on the homogeneity of local regions of an image. However, we think that the complexity of local regions of an image is more essential than the homogeneity, because we pay close attention to complex region than homogeneous region. Therefore, complex regions are essential for image compression. Since the complexity of regions of an image is quantified by values of LFD, we implemented variable block size using LFD values and constructed a codebook (CB) for a VQ. To confirm the performance of the proposed method, we only used a discriminant analysis and FGLA to construct a CB. Here, the FGLA is the algorithm to combine generalized Lloyd algorithm (GLA) and the fuzzy k means algorithm. Results of computational experiments showed that this method correctly encodes the regions that we pay close attention. This is a promising result for obtaining a well-perceived compressed image. Also, the performance of the proposed method is superior to that of VQ by FGLA in terms of both compression rate and decoded image quality. Furthermore, 1.0 bpp and more than 30 dB in PSNR by a CB with only 252 code-vectors were achieved using this method.

A fast VQ codebook generation algorithm using codeword displacement

In this paper, we present a fast codebook generation algorithm called CGAUCD (Codebook Generation Algorithm Using Codeword Displacement) by making use of the codeword displacement between successive partition processes. By implementing a fast search algorithm named MFAUPI (Modified Fast Algorithm Using Projection and Inequality) for VQ encoding in the partition step of CGAUCD, the codebook generation time can be further reduced significantly. Using MFAUPI, the computing time of CGAUCD can be reduced by a factor of 4.7–7.6. Compared to Generalized Lloyd Algorithm (GLA), our proposed method can reduce the codebook generation time by a factor of 35.9–121.2. Compared to the best codebook generation algorithm to our knowledge, our approach can further reduce the corresponding computing time by 26.0–32.8%. It is noted that our proposed algorithm can generate the same codebook as that produced by the GLA. The superiority of our method is more remarkable when a larger codebook is generated.

Quantization Methods for Equal Gain Transmission With Finite Rate Feedback

We consider the design and analysis of quantizers for equal gain transmission (EGT) systems with finite rate feedback-based communication in flat-fading multiple input single output (MISO) systems. EGT is a beamforming technique that maximizes the MISO channel capacity when there is an equal power-per-antenna constraint at the transmitter, and requires the feedback of t-1 phase angles, when there are t antennas at the transmitter. In this paper, we contrast two popular approaches for quantizing the phase angles: vector quantization (VQ) and scalar quantization (SQ). On the VQ side, using the capacity loss with respect to EGT with perfect channel information at transmitter as performance metric, we develop a criterion for designing the beamforming codebook for quantized EGT (Q-EGT). We also propose an iterative algorithm based on the well-known generalized Lloyd algorithm, for computing the beamforming vector codebook. On the analytical side, we study the performance of Q-EGT and derive closed-form expressions for the performance in terms of capacity loss and outage probability in the case of i.i.d. Rayleigh flat-fading channels. On the SQ side, assuming uniform scalar quantization and i.i.d. Rayleigh flat-fading channels, we derive the high-resolution performance of quantized EGT and contrast the performance with that of VQ. We find that although both VQ and SQ achieve the same rate of convergence (to the capacity with perfect feedback) as the number of feedback bits B increases, there exists a fixed gap between the two

3D Model Retrieval Based on Vector Quantisation Index Histograms

This paper proposes a novel technique to retrieval 3D mesh models using vector quantisation index histograms. Firstly, points are sampled uniformly on mesh surface. Secondly, to a point five features representing global and local properties are extracted. Thus feature vectors of points are obtained. Third, we select several models from each class, and employ their feature vectors as a training set. After training using LBG algorithm, a public codebook is constructed. Next, codeword index histograms of the query model and those in database are computed. The last step is to compute the distance between histograms of the query and those of the models in database. Experimental results show the effectiveness of our method.

Fast vector quantization algorithms based on nearest partition set search

A fast search method for vector quantization is proposed in this paper. It makes use of the fact that in the generalized Lloyd algorithm (GLA) a vector in a training sequence is either placed in the same minimum distance partition (MDP) as in the previous iteration or in a partition within a very small subset of partitions. The proposed method searches for the MDP for a training vector only in this subset of partitions plus the single previous MDP. As the size of this subset is much smaller than the total number of codevectors, the search process is speeded up significantly. The creation of the subset is essential, as it has a direct effect on the improvement in computation time of the proposed method. The schemes that create the subset efficiently have been proposed. The proposed method generates a codebook identical to that generated using the GLA. It is simple and requires only minor modification of the GLA and a modest amount of additional memory. The experimental results show that the computation time of codebook training was improved by factors from 6.6 to 50.7 and from 5.8 to 70.4 for two test data sets when codebooks of sizes from N = 16 to 2048 were trained. The proposed method was also combined with an earlier published method to further improve the computation time.

Image Compression Using Wavelet Transform and Vector Quantization

In this paper a modified LBG vector quantization alglrithm id presented and used with wavelet transform algorithm to compress the image. The modified LBG algorithm provides good representation for training vectors carrying important information which may repesent rare vectors in the training vectors. So this algorithm overcomes the problem of the traditional LBG algorithm that provides good representation for training vectors which occur frequently while rare vectors are not well represented. The use of the modified LBG algorithm with wavelet transform give better reconstructed image quality than the traditional LBG algorithm because the rare vectors hich carry important information are better represented. The two algorithm give the same compression ratio.