Thek-means clustering algorithm is a commonly used algorithm for palette design. If an adequate initial palette is selected, a good quality reconstructed image of a compressed colour image can be achieved. The major p...
详细信息
Thek-means clustering algorithm is a commonly used algorithm for palette design. If an adequate initial palette is selected, a good quality reconstructed image of a compressed colour image can be achieved. The major problem is that a great deal of computational cost is consumed. To accelerate thek-means clustering algorithm, two test conditions are employed in the proposed algorithm. From the experimental results, it is found that the proposed algorithm significantly cuts down the computational cost of thek-means clustering algorithm without incurring any extra distortion.
The vector quantization was a powerful technique in image compression. The widely used method such as the Linde-Buzo-Gray (lbg) algorithm always generated local optimal codebook. Recently, particle swarm optimization ...
详细信息
The vector quantization was a powerful technique in image compression. The widely used method such as the Linde-Buzo-Gray (lbg) algorithm always generated local optimal codebook. Recently, particle swarm optimization was adapted to obtain the near-global optimal codebook of vector quantization. The alterative method called the quantum particle swarm optimization was developed to improve the results of original PSO algorithm. The honey bee mating optimization was used to develop the algorithm for vector quantization. In this paper, we proposed a new method based on the artificial bee colony (ABC) algorithm to construct the codebook of vector quantization. The proposed method uses lbg method as the initial of ABC algorithm to develop the VQ algorithm. This method is called ABClbg algorithm. The ABC-lbg algorithm is compared with four algorithms described above. Experimental results showed that the ABC-lbg algorithm is more reliable and the reconstructed images get higher quality compared to other methods.
The vector quantization was a powerful technique in image *** widely used method such as the Linde-Buzo-Gray(lbg)algorithm always generated local optimal codebook. Recently,particle swarm optimization was adapted to o...
详细信息
The vector quantization was a powerful technique in image *** widely used method such as the Linde-Buzo-Gray(lbg)algorithm always generated local optimal codebook. Recently,particle swarm optimization was adapted to obtain the near-global optimal codebook of vector *** alterative method called the quantum particle swarm optimization was developed to improve the results of original PSO *** honey bee mating optimization was used to develop the algorithm for vector *** this paper,we proposed a new method based on the artificial bee colony(ABC) algorithm to construct the codebook of vector *** proposed method uses lbg method as the initial of ABC algorithm to develop the VQ *** method is called ABC-lbg *** ABC-lbg algorithm is compared with four algorithms described above. Experimental results showed that the ABC-lbg algorithm is more reliable and the reconstructed images get higher quality compared to other methods.
The speaker recognition is a sort of biometrics according to person's sound. This paper proposed a method that extracted characteristic parameter from sound signal by LPCC and MFCC. Improving lbg algorithm, traini...
详细信息
ISBN:
(纸本)9781510806481
The speaker recognition is a sort of biometrics according to person's sound. This paper proposed a method that extracted characteristic parameter from sound signal by LPCC and MFCC. Improving lbg algorithm, training and testing the samples by continuous left-right HMM, A speaker recognition algorithm was given. Trough experiment, the result in 4 changes continuous left-right HMM is best.
The hearing environment recognition algorithm is one of the core algorithms of the modern digital hearing aids. It can recognize the quiet, noisy and musical environment under a variety of scenarios, such as street, t...
详细信息
ISBN:
(纸本)9781467376839
The hearing environment recognition algorithm is one of the core algorithms of the modern digital hearing aids. It can recognize the quiet, noisy and musical environment under a variety of scenarios, such as street, train or restaurant scene in noise environment. It then adjusts the parameters of other hearing aids algorithms according to different audio environment. This paper presents a method to improve the training algorithm of Gaussian mixture models(GMMs), which uses fusion lbg clustering algorithm and annealing algorithm to initialize the value of GMM parameter means μ, instead of initializing it randomly in traditional way. The method can improve recognition accuracy by optimizing the GMM parameters. The experimental result demonstrates that the accuracy rate of the traditional GMMs algorithm is about 77%, while that of the method we proposed is about 85%. In the end, we continue to explore the influence of the selection of the MFCC features and Gaussian mixture numbers on the recognition rate.
暂无评论