Fast Codebook Generation Algorithm Research Articles

Content based Video Retrieval Systems Performance based on Multiple Features and Multiple Frames using SVM

In this paper, Content Based Video Retrieval Systems performance is analysed and compared for three different types of feature vectors. These types of features are generated using three different algorithms; Block Truncation Coding (BTC) extended for colors, Kekre’s Fast Codebook Generation (KFCG) algorithm and Gabor filters. The feature vectors are extracted from multiple frames instead of using only key frames or all frames from the videos. The performance of each type of feature is analysed by comparing the results obtained by two different techniques; Euclidean Distance and Support Vector Machine (SVM). Although a significant number of researchers have expressed dissatisfaction to use image as a query for video retrieval systems, the techniques and features used here provide enhanced and higher retrieval results while using images from the videos. Apart from higher efficiency, complexity has also been reduced as it is not required to find key frames for all the shots. The system is evaluated using a database of 1000 videos consisting of 20 different categories. Performance achieved using BTC features calculated from color components is compared with that achieved using Gabor features and with KFCG features. These performances are compared again with the performances obtained from systems using SVM and the systems without using SVM.

K-Means Codebook Optimization using KFCG Clustering Technique

Codebook Optimization is a concept of vector quantization which is applied to achieve lossy compression. Optimization of the codebook helps in maintaining the quality of the image. The codebook is generated using Kekre’s Fast Codebook Generation (KFCG) algorithm and Random Selection Method. The K-means algorithm is used to optimize the codebook. The Mean Square Error (MSE) is used as the measurement parameter. The point where the MSE converges is the optimal point and the codebook at that point is said to be the optimized codebook. The results obtained show that when K-means algorithm is applied to the codebook generated by KFCG Algorithm, less MSE is obtained as compared to Random Selection Method.

Codebook Design for Vector Quantization Based on a Kernel Fuzzy Learning Algorithm

Vector quantization (VQ) is an efficient technique for data compression and has been successfully used in various applications. The methods most commonly used to generate a codebook are the Linde, Buzo, Gray (LBG) algorithm, fuzzy vector quantization (FVQ) algorithm, Kekre‘s Fast Codebook Generation (KFCG) algorithm, discrete cosine transform based (DCT-based) codebook generation method, and k-principle component analysis (K-PCA) algorithm. However, if the separation boundaries in codebook generation are nonlinear, their performance can degrade fast. In this paper, we present a kernel fuzzy learning (KFL) algorithm, which takes advantages of the distance kernel trick and the gradient-based fuzzy clustering method, to create a codebook automatically. Experiments with real data show that the proposed algorithm is more efficient in its performance compared to that of the LBG, FVQ, KFCG, and DCT-based method, and to the K-PCA algorithm.

Comparative Analysis of Automatic Speaker Recognition using Kekre’s Fast Codebook Generation Algorithm in Time and Transform Domain

In this paper, an approach based on Kekre‟s fast code book Generation (KFCG) Algorithm in the transform domain has been proposed. KFCG is used for feature extraction in both the training and testing phases. Three methods for codebook generation have been used. In the 1 st method, codebooks are generated from the speech samples by using Discrete Fourier Transform (DFT). In the 2 nd method, the codebooks are generated using Discrete Cosine Transform (DCT). In the 3 rd method, the codebooks are generated using the Discrete Sine Transform (DST). For speaker identification, the codebook of the test sample is similarly generated and compared with the codebooks of the reference samples stored in the database. The results obtained for the above methods in the transform domain are compared with the results obtained in the time domain analysis. The results show that KFCG gives better results in transform domain than in time domain. Also the results improve as the vector dimension while generating the codebook is increased.

Performance Comparison of Speaker Recognition using Vector Quantization by LBG and KFCG

In this paper, two approaches for speaker Recognition based on Vector quantization are proposed and their performances are compared. Vector Quantization (VQ) is used for feature extraction in both the training and testing phases. Two methods for codebook generation have been used. In the 1 st method, codebooks are generated from the speech samples by using the Linde-Buzo-Gray (LBG) algorithm. In the 2 nd method, the codebooks are generated using the Kekre‟s Fast Codebook Generation (KFCG) algorithm. For speaker identification, the codebook of the test sample is similarly generated and compared with the codebooks of the reference samples stored in the database. The results obtained for both the methods have been compared. The results show that KFCG gives better results than LBG.

A fast VQ codebook generation algorithm via pattern reduction

In this paper, we present a simple but fast codebook generation algorithm, called PREGLA (Pattern Reduction Enhanced GLA). The proposed algorithm is fundamentally different from the previous approaches in that the previous approaches focus on reducing the size of the codebook whereas the proposed algorithm focuses on using pattern reduction to reduce the computation time. The proposed algorithm is motivated by the observation that input vectors that are “static” during the training process can be considered as part of the final solutions and thus can be compressed and removed to eliminate the redundant computations at the later iterations of the training process. To evaluate the performance of the proposed algorithm, we compare the proposed algorithm with “efficient” state-of-the-art GLA or GLA-based algorithms such as Codeword Displacement, Nearest Partition Set Search, Fast Vector Quantization Algorithm, Law of Cosines, and standard GLA. Our experimental results indicate that the proposed algorithm can reduce the computation time from 29.45% up to about 77.98% compared to those of standard GLA and other fast GLA-based algorithms alone.

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.

Fast Search Algorithms for VQ Codebook Generation

In this paper, we propose two fast codebook generation algorithms by making use of the information in the iterative process. Comparing to the conventional full search method (the LBG algorithm), the Fast Codebook Generation Algorithm (FCGA) reduces the CPU time by a factor of 2.49 to 6.16 for real images. Compared to FCGA the Maximum Set Algorithm (MSA), which is designed for the large codebook, gives the worse performance in general for our simulations. However, the Maximum Set Algorithm (MSA) still gives a better performance than that of the LBG algorithm and reduces the CPU time by a factor of 1.63 to 2.67. Both methods presented in this paper outperform the DSBS method developed by Huang and Harris. Compared to the LBG algorithm, the DSBS method reduced the codebook generation time by a factor of 1.5–2. It is noted that both FCGA and MSA give the same codebook as that produced by the LBG algorithm. Finally, some experimental results for several training sets with different types and sizes are listed.

Fast codebook generation algorithm for vector quantization of images

A partial search partial distortion (PSPD) algorithm is presented for reducing the computational complexity of the codebook generation in vector quantization (VQ) of images. A partial codebook is built up using the feature of a training vector. The partial distortion technique is employed to find the best match codevector from the partial codebook. The new algorithm results in a considerable reduction of computational complexity when compared to the conventional LBG algorithm.