Image vector quantization with variable dimension blocks and edge preserving cost function

Selvi, S. Sethu; Makur, Anamitra

doi:10.1016/s0165-1684(03)00117-8

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Discrete observations often occur in signal processing applications where the quantization of continuous values is necessary [34]. Digital communications and image processing are some fields where quantization is crucially important [35,36] to limit the size of the storage or to describe a digital content with only a few features. Wireless sensor networks are characterized by limited resources, such as energy and communication bandwidth.…”

Section: Discrete Observations and Finite Linear Programmingmentioning

confidence: 99%

Constructive minimax classification of discrete observations with arbitrary loss function

Fillatre

2017

Signal Processing

View full text Add to dashboard Cite

This paper develops a multihypothesis testing framework for calculating numerically the optimal minimax test with discrete observations and an arbitrary loss function. Discrete observations are common in data processing and make tractable the calculation of the minimax test. Each hypothesis is both associated to a parameter defining the distribution of the observations and to an action which describes the decision to take when the hypothesis is true. The loss function measures the gap between the parameters and the actions. The minimax test minimizes the maximum classification risk. It is the solution of a finite linear programming problem which gives the worst case classification risk and the worst case prior distribution. The minimax test equalizes the classification risks whose prior probabilities are strictly positive. The minimax framework is applied to vector channel decoding which consists in classifying some codewords transmitted on a binary asymmetric channel. The Hamming metric is used to measure the number of differences between the emitted codeword and the decoded one.

show abstract

Section: Discrete Observations and Finite Linear Programmingmentioning

confidence: 99%

Constructive minimax classification of discrete observations with arbitrary loss function

Fillatre

2017

Signal Processing

View full text Add to dashboard Cite

show abstract

Image vector quantization with variable dimension blocks and edge preserving cost function

Cited by 1 publication

References 8 publications

Constructive minimax classification of discrete observations with arbitrary loss function

Constructive minimax classification of discrete observations with arbitrary loss function

Contact Info

Product

Resources

About