Classification of face images using local iterated function systems
Machine Vision and Applications
A multiexpert collaborative biometric system for people identification
Journal of Visual Languages and Computing
Normal maps vs. visible images: Comparing classifiers and combining modalities
Journal of Visual Languages and Computing
Fractal capacity dimension of three-dimensional histogram from color images
Multidimensional Systems and Signal Processing
Embedding linear transformations in fractal image coding
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Fine: fractal indexing based on neighborhood estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Occluded face recognition by means of the IFS
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
| Hi-index | 0.01 |
As already documented in the literature, fractal image encoding is a family of techniques that achieves a good compromise between compression and perceived quality by exploiting the self-similarities present in an image. Furthermore, because of its compactness and stability, the fractal approach can be used to produce a unique signature, thus obtaining a practical image indexing system. Since fractal-based indexing systems are able to deal with the images in compressed form, they are suitable for use with large databases. We propose a system called FIRE, which is then proven to be invariant under three classes of pixel intensity transformations and under geometrical isometries such as rotations by multiples of π/2 and reflections. This property makes the system robust with respect to a large class of image transformations that can happen in practical applications: the images can be retrieved even in the presence of illumination and/or color alterations. Additionally, the experimental results show the effectiveness of FIRE in terms of both compression and retrieval accuracy.