Kohonen's Self Organizing Feature Maps with variable learning rate. Application to image compression

Cziho, A.; Solaiman, Basel; Cazuguel, Guy; Roux, Christian; Lovanyi, I.

doi:10.1016/b978-044482587-2/50005-7

Cited by 8 publications

(1 citation statement)

References 4 publications

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“…(15) to find its corresponding neuron w x . The SOM may be considered to as a non-uniform quantization of the feature space [18]. This non-uniform quantization performed by Kohonen's map has the advantage to make the class definition on the map (i.e.…”

Section: ) Training Phasementioning

confidence: 99%

Kohonen’s Map Approach for the Belief Mass Modeling

Hammami

Mercier

Hamouda

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

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In the framework of the evidence theory, several approaches for estimating belief functions are proposed. However, they generally suffer from the problem of masses attribution in the case of compound hypotheses that lose much conceptual contribution of the theory. In this paper, an original method for estimating mass functions using Kohonen's map derived from the initial feature space and an initial classifier is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses but also for disjunctions and conjunctions of hypotheses. Thus, it can model at the same time ignorance, imprecision, and paradox. The proposed method for a basic belief assignment (BBA) is of interest for solving estimation mass functions problems where a large quantity of multivariate data is available. Indeed, the use of Kohonen's map simplifies the process of assigning mass functions. The proposed method has been compared with the state-of-the-art BBA technique on benchmark database and applied on remote sensing data for image classification purpose. Experimentation shows that our approach gives similar or better results than other methods presented in the literature so far, with an ability to handle a large amount of data.

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Section: ) Training Phasementioning

confidence: 99%

Kohonen’s Map Approach for the Belief Mass Modeling

Hammami

Mercier

Hamouda

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

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Joint Classification and Compression of Hyperspectral Images

Mercier¹,

Mouchot²,

Cazuguel³

Hyperspectral Data Compression

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Derailment-free finite state vector quantization using conditional histogram. Optimization and application to image compression

Cziho

Lovanyi²,

Solaiman³

et al.

Proceedings of International Conference on Image Processing

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In the present paper the Finite State Vector Quantization (FSVQ) image compression approach is studied. A new compression scheme is proposed which optimizes the performance of the so-called Conditional Histogram next-state function design. The optimization is performed by determining for every input block the state codebook size, that minimizes the expected value of the number of bits in the compressed dataflow. This is done under the constraint to ensure the same reconstruction quality as that of the full-search vector quantizer. The derailment is avoided by transmitting side information to the decoder. The proposed scheme is tested on still medical endoscopic images. Since the system is always adapted to the probability distribution of block occurrences, it provides lower bit rate than FSVQ that applies state codebooks offixed size.

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Kohonen's Self Organizing Feature Maps with variable learning rate. Application to image compression

Cited by 8 publications

References 4 publications

Kohonen’s Map Approach for the Belief Mass Modeling

Kohonen’s Map Approach for the Belief Mass Modeling

Joint Classification and Compression of Hyperspectral Images

Derailment-free finite state vector quantization using conditional histogram. Optimization and application to image compression

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