Elements of information theory
Elements of information theory
The handbook of brain theory and neural networks
Gabor wavelets for statistical pattern recognition
The handbook of brain theory and neural networks
Mixtures of probabilistic principal component analyzers
Neural Computation
An Introduction to Natural Computation
An Introduction to Natural Computation
Digital Image Processing
Topographic Independent Component Analysis
Neural Computation
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
High capacity watermarking in nonedge texture under statistical distortion constraint
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Spread-spectrum watermark by synthesizing texture
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
A hierarchical vision processing architecture oriented to 3D integration of smart camera chips
Journal of Systems Architecture: the EUROMICRO Journal
Hi-index | 0.14 |
We develop a new biologically motivated algorithm for representing natural images using successive projections into complementary subspaces. An image is first projected into an edge subspace spanned using an ICA basis adapted to natural images which captures the sharp features of an image like edges and curves. The residual image obtained after extraction of the sharp image features is approximated using a mixture of probabilistic principal component analyzers (MPPCA) model. The model is consistent with cellular, functional, information theoretic, and learning paradigms in visual pathway modeling. We demonstrate the efficiency of our model for representing different attributes of natural images like color and luminance. We compare the performance of our model in terms of quality of representation against commonly used basis, like the discrete cosine transform (DCT), independent component analysis (ICA), and principal components analysis (PCA), based on their entropies. Chrominance and luminance components of images are represented using codes having lower entropy than DCT, ICA, or PCA for similar visual quality. The model attains considerable simplification for learning from images by using a sparse independent code for representing edges and explicitly evaluating probabilities in the residual subspace.