Efficient color image compression using integrated fuzzy neural networks for vector quantization

Mitra, Sunanda; Pemmaraju, Suryalakshmi; Kompella, Sastry; Meadows, S.

doi:10.1109/icsmc.1997.625746

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Both IAFC and AFLC have been used extensively for precise classification of a variety of data sets. More recently, these models have demonstrated their ability to perform vector quantization [7] in the wavelet-transformed domain for encoding very large color images [16][17].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Clustering for Efficient Segmentation of Wellbore Resistivity Images

Jose¹,

Carlos²,

Johnny³

2002

Proceedings of SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractResistivity image segmentation allows mapping of similar regions in a scene leading to the recognition of distinct geological features such as rock fractures and thin beds. Recognition of similar patterns embedded in resistivity image data is the basis of clustering regions in an image. Therefore an efficient clustering technique is a natural choice for image segmentation. Efforts to develop algorithms for adaptive and less computationally complex classification of data have led to the implementation of neural network classifiers of both supervised and unsupervised learning. Such neural network architectures are ways to achieve autonomous processing of patterns but are not considered to incorporate intelligent decision processes offered by various techniques of fuzzy clustering. Integration of fuzzy membership values of objects into neural network processing generates more robust models for intelligent pattern recognition.This paper presents and efficient object extraction method for resistivity image segmentation based on neuro-fuzzy clustering algorithms. The neuro-fuzzy approach retains the basic properties and architectures of neural networks and fuzzifies some other elements, enhancing the learning speed and generalization capabilities.

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Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Clustering for Efficient Segmentation of Wellbore Resistivity Images

Jose¹,

Carlos²,

Johnny³

2002

Proceedings of SPE Annual Technical Conference and Exhibition

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“…Recently, vector quantization of color coordinates of wavelet coefficients has been proposed in [7] and [8] as an attractive way to compress color images. In [8], magnitude ordering information for vectors of color coordinates of wavelet coefficients is coded by linking these vectors by the Spatial Orientation Trees (SOT) of Set Partitioning in Hierarchical Trees (SPIHT, [9]) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Linear filtering of image subbands for low complexity postprocessing of decoded color images

Bayazıt

2005

SPIE Proceedings

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In [1], image adaptive linear minimum mean squared error (LMMSE) filtering was proposed as an enhancement layer color image coding technique that exploited the statistical dependencies among the luminance/chrominance or Karhunen Loeve Transform (KLT) coordinate planes of a lossy compressed color image to enhance the red, blue, green (RGB) color coordinate planes of that image. In the current work, we propose the independent design and application of LMMSE filters on the subbands of a color image as a low complexity solution. Towards this end, only the coordinates of the neighbors of the filtered subband coefficient, that are sufficiently correlated with the corresponding coordinate of the filtered subband coefficient, are included in the support of the filter for each subband. Additionally, each subband LMMSE filter is selectively applied only on the high variance regions of the subband. Simulation results show that, at the expense of an insignificant increase in the overhead rate for the transmission of the coefficients of the filters and with about the same enhancement gain advantage, subband LMMSE filtering offers a substantial complexity advantage over fullband LMMSE filtering.

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“…This paper demonstrates that adaptive clustering techniques involving integrated neural fuzzy systems can be applied to solve difficult problems in traditional signdimage processing such as segmentation of multisensor images that aids in automated registration and fusion. The application of such adaptive integrated clustering techniques to very low bit rate image coding by vector quantization has already been demonstrated [16,17].…”

Section: Discussionmentioning

confidence: 99%

Adaptive clustering for segmentation of multi-sensor images

Mitra

Dickens²,

Pemmaraju³

1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98C

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Automatic detection or recognition of specific objects from a sequence of images acquired under varying conditions and from different modalities requires careful segmentation. We present here the use of an adaptive neuro-fuzzy clustering technique for image segmentation and boundary detection for better extraction of the dominant features in images of the same scene acquired by synthetic aperture radar (SAR) and electro-optic (EO) sensors. The advantage of such adaptive segmentation is clearer identification of objects that appear differently due to different sensor characteristics.

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Efficient color image compression using integrated fuzzy neural networks for vector quantization

Cited by 8 publications

References 18 publications

Adaptive Fuzzy Clustering for Efficient Segmentation of Wellbore Resistivity Images

Adaptive Fuzzy Clustering for Efficient Segmentation of Wellbore Resistivity Images

Linear filtering of image subbands for low complexity postprocessing of decoded color images

Adaptive clustering for segmentation of multi-sensor images

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