Combining image classification and image compression using vector quantization

Oehler, K.L.; Gray, Robert M.

doi:10.1109/dcc.1993.253150

Cited by 29 publications

(15 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The issue of joint compression and classification was considered and studied extensively by Oehler, Gray, Perlmutter, Olshen, et al [13,14,16,17,15,18,19,11,6]. A vector quantizer aimed at combining compression and classification generates indices that are mapped into both representative codewords and classes for original vectors at the receiving end.…”

Section: Introductionmentioning

confidence: 99%

“…The joint compression and classification algorithm developed by Oehler, Gray, Perlmutter, Olshen, et al [13,14,16,17,15,18,19], is referred to as Bayes VQ. The basic assumption is that a training sequence L = {(x i , y i ), i = 1, 2, ..., L} is a realization of a random process {(X i , Y i ), i = 1, 2, ...} with (X i , Y i ) obeying a common but unknown distribution P XY on (X, Y ) ∈ A X × A Y .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint image compression and classification with vector quantization and a two dimensional hidden Markov model

Gray

Olshen

1999

Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096)

Self Cite

View full text Add to dashboard Cite

We present an algorithm to achieve good compression and classification for images using vector quantization and a two dimensional hidden Markov model. The feature vectors of image blocks are assumed to be generated by a two dimensional hidden Markov model. We first estimate the parameters of the model, then design a vector quantizer to minimize a weighted sum of compression distortion and classification risk, the latter being defined as the negative of the maximum log likelihood of states and feature vectors. The algorithm is tested on both synthetic data and real image data. The extension to joint progressive compression and classification is discussed.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint image compression and classification with vector quantization and a two dimensional hidden Markov model

Gray

Olshen

1999

Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096)

Self Cite

View full text Add to dashboard Cite

show abstract

“…(1). The respective problem of generative (representative) models versus classification property was generally discussed for vector quantization [9,37,38,39]. In consequence, if a generative GLVQ models is strictly demanded, one has to add a respective penalty term to the cost function according to…”

Section: Class Typical Prototypes Versus Class Border-sensitive Lvq Vmentioning

confidence: 99%

“…Moreover, keeping in mind the rapidly growing number of available data, classification learning is demanded to handle big data leading to the related task of data compression. However, classification learning and data compression of vector data are the two different sides of one coin, which are closely related to supervised and unsupervised vector quantization [9]. Beside other paradigms, prototype based approaches are known to be robust machine learning methods with high performance, in general, while being often easy to interpret [10].…”

Section: Introductionmentioning

confidence: 99%

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Villmann

Bohnsack

Kaden

2016

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classification of vector data, intuitively introduced by Kohonen. The prototype adaptation scheme relies on its attraction and repulsion during the learning providing an easy geometric interpretability of the learning as well as of the classification decision scheme. Although deep learning architectures and support vector classifiers frequently achieve comparable or even better results, LVQ models are smart alternatives with low complexity and computational costs making them attractive for many industrial applications like intelligent sensor systems or advanced driver assistance systems.Nowadays, the mathematical theory developed for LVQ delivers sufficient justification of the algorithm making it an appealing alternative to other approaches like support vector machines and deep learning techniques.This review article reports current developments and extensions of LVQ starting from the generalized LVQ (GLVQ), which is known as the most powerful cost function based realization of the original LVQ. The cost function minimized in GLVQ is an soft-approximation of the standard classification error allowing gradient descent learning techniques. The GLVQ variants considered in this contribution, cover many aspects like bordersensitive learning, application of non-Euclidean metrics like kernel distances or divergences, relevance learning as well as optimization of advanced statistical classification quality measures beyond the accuracy including sensitivity and specificity or area under the ROC-curve.According to these topics, the paper highlights the basic motivation for these variants and extensions together with the mathematical prerequisites and treatments for integration into the standard GLVQ scheme and compares them to other machine learning approaches. For detailed description and mathematical theory behind all, the reader is referred to the respective original articles.Thus, the intention of the paper is to provide a comprehensive overview of the stateof-the-art serving as a starting point to search for an appropriate LVQ variant in case of a given specific classification problem as well as a reference to recently developed variants and improvements of the basic GLVQ scheme.

show abstract

“…Another approach to incorporating class information in the design of prototypes is the Bayes Vector Quantization BVQ algorithm proposed by Oehler, Gray, Perlmutter, Olshen et al 14,15 . BVQ aims at achieving good compression and classication simultaneously.…”

Section: Introductionmentioning

confidence: 99%

A source coding approach to classification by vector quantization and the principle of minimum description length

Proceedings DCC 2002. Data Compression Conference

View full text Add to dashboard Cite

An algorithm for supervised classi cation using vector quantization and entropy coding is presented. The classi cation rule is formed from a set of training data fX i ; Y i g n i=1 , which are independent samples from a joint distribution P X Y . Based on the principle of Minimum Description Length MDL, a statistical model that approximates the distribution P X Y ought to enable ecient coding of X and Y . On the other hand, we expect a system that encodes X;Y e ciently to provide ample information on the distribution P X Y . This information can then be used to classify X , i.e., to predict the corresponding Y based on X . To encode both X and Y , a t wo-stage vector quantizer is applied to X and a Hu man code is formed for Y conditioned on each quantized value of X . The optimization of the encoder is equivalent to the design of a vector quantizer with an objective function re ecting the joint penalty of quantization error and misclassi cation rate. This vector quantizer provides an estimation of the conditional distribution of Y given X , which in turn yields an approximation to the Bayes classi cation rule. This algorithm, namely Discriminant Vector Quantization DVQ, is compared with Learning Vector Quantization LVQ and CART R on a number of data sets. DVQ outperforms the other two on several data sets. The relation between DVQ, density estimation, and regression is also discussed. I IntroductionThe goal of supervised classi cation is to form decision rules based on a set of training data so that misclassi cation penalty is minimized. Assume the training set is L = fx i ; y i ; i = 1; 2; :::; ng, where x i ; y i 's are independent samples from a joint distribution P X Y , X 2 X, Y 2 Y. Typically X is a continuous random variable

show abstract

Combining image classification and image compression using vector quantization

Cited by 29 publications

References 10 publications

Joint image compression and classification with vector quantization and a two dimensional hidden Markov model

Joint image compression and classification with vector quantization and a two dimensional hidden Markov model

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

A source coding approach to classification by vector quantization and the principle of minimum description length

Contact Info

Product

Resources

About