While vector quantization (VQ) has been applied widely to generate features for visual recognition problems, much recent work has focused on more powerful methods. In particular, sparse coding has emerged as a strong alternative to traditional VQ approaches and has been shown to achieve consistently higher performance on benchmark datasets. Both approaches can be split into a training phase, wh...

It is known that learning methods of fuzzy inference systems using vector quantization (VQ) and steepest descend method (SDM) are superior in terms of the number of rules. However, they need a great deal of learning time. The cause could be that both of VQ and SDM perform only local searches. On the other hand, it has been shown that a learning method of radial basis function (RBF) networks usi...

There is a critical need to develop diagnostic procedures enabling early detection of tumors while at a curable stage. Technetium 99m (99mTc)-labeled VQ peptide (99mTc-HYNIC-VQ) identified through screening phage display peptide libraries against fresh human colonic adenomas was prepared and evaluated for tumor detection. 99mTc-HYNIC-VQ was prepared by a non-SnCl2 method with more than 99% radi...

Visual (image and video) database systems require efficient indexing to enable fast access to the images in a database. In addition, the large memory capacity and channel bandwidth requirements for the storage and transmission of visual data necessitate the use of compression techniques. Vector quantization (VQ) is an efficient technique for low bit-rate image and video compression. In addition...

In this paper, a novel face recognition system using Vector quantization (VQ) technique is proposed. Four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to generate codebooks of desired size. Euclidean distance is used as similarity measure to compare the feature vector of test image with that of trainee images. Proposed algorithms are tested on two different databases. One is ...

VQ motif-containing proteins play crucial roles in abiotic stress responses in plants. Recent studies have shown that some VQ proteins physically interact with WRKY transcription factors to activate downstream genes. In the present study, we identified and characterized genes encoding VQ motif-containing proteins using the most recent version of the maize genome sequence. In total, 61VQ genes w...

| In this paper we consider Principal Component Analysis (PCA) and Vector Quantization (VQ) neural networks for image compression. We present a method where the PCA and VQ steps are adaptively combined. A learning algorithm for this combined network is derived. We demonstrate that this approach can improve the results of the successive application of the individually optimal methods .

This paper shows a way to combine speech recognition techniques based on Vector Quantization (VQ) with Neural Networks (NN). Vector Quantization has proved its usefulness for isolated words recognition, but it is also useful for isolated sentences recognition. One way to improve the performance of this technique is to add an NN block that will help the performance of the VQ recognizer.

Abstract In this paper, we show that Icelandic uses the phonetic parameters of speaking rate, duration and voice quality (VQ) to distinguish between information-seeking questions (ISQs) rhetorical (RQs). Specifically, durations are longer (speaking rate is slower) nonmodal VQs used more in RQs than ISQs. Our findings for temporal fit with previous studies on prosody various languages. With resp...