Dimension reduction by local principal component analysis
Neural Computation
Image Compression Using KLT, Wavelets and an Adaptive Mixture of Principal Components Model
Journal of VLSI Signal Processing Systems - special issue on applications of neural networks in biomedical image processing
Mixtures of probabilistic principal component analyzers
Neural Computation
Self-Organized Feature Extraction Achieved with a Parameterized Filterbank
Neural Processing Letters
An adaptive lapped biorthogonal transform and its application in orientation adaptive image coding
Signal Processing - Image and Video Coding beyond Standards
DCC '01 Proceedings of the Data Compression Conference
Combining spatial and colour information for content based image retrieval
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
A new integer image coding technique based on orthogonal polynomials
Image and Vision Computing
Probabilistic PCA self-organizing maps
IEEE Transactions on Neural Networks
A framework for fully configurable video coding
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
A fractional bit encoding technique for the GMM-based block quantisation of images
Digital Signal Processing
Dynamic replacement of video coding elements
Image Communication
Implementing fully configurable video coding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Still image coding using a wavelet-like transform
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Edge detection in the feature space
Image and Vision Computing
Source Coding: Part I of Fundamentals of Source and Video Coding
Foundations and Trends in Signal Processing
Image compression by vector quantization with recurrent discrete networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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The optimal linear block transform for coding images is well known to be the Karhunen-Loeve transformation (KLT). However, the assumption of stationarity in the optimality condition is far from valid for images. Images are composed of regions whose local statistics may vary widely across an image. While the use of adaptation can result in improved performance, there has been little investigation into the optimality of the criterion upon which the adaptation is based. In this paper we propose a new transform coding method in which the adaptation is optimal. The system is modular, consisting of a number of modules corresponding to different classes of the input data. Each module consists of a linear transformation, whose bases are calculated during an initial training period. The appropriate class for a given input vector is determined by the subspace classifier. The performance of the resulting adaptive system is shown to be superior to that of the optimal nonadaptive linear transformation. This method can also be used as a segmentor. The segmentation it performs is independent of variations in illumination. In addition, the resulting class representations are analogous to the arrangement of the directionally sensitive columns in the visual cortex