Fractals everywhere
Practical neural network recipes in C++
Practical neural network recipes in C++
Efficacy of fractal features in segmenting images of natural textures
Pattern Recognition Letters
Use of IFS codes for learning 2D isolated-object classification systems
Computer Vision and Image Understanding
Optimal Fractal Coding is NP-Hard
DCC '97 Proceedings of the Conference on Data Compression
Character Representation and Recognition Using Quadtree-based Fractal Encoding Scheme
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Technique for fractal image compression using genetic algorithm
IEEE Transactions on Image Processing
A fast and efficient hybrid fractal-wavelet image coder
IEEE Transactions on Image Processing
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The main aim of the paper is to present the authors' original method of feature generation from digital images and to report on a comparison of five various algorithms, which implemented that method. The algorithms are based on an idea by the same authors', which consists in producing a quantitative description of similarity intensity between various parts of an image in various scales. To develop it the algorithms take advantage of fractal coding based on an Iterated Function System. Therefore, the generated features can rightly be called similarity features. In this paper we show that similarity features, when combined with other well known ones, can improve recognition results in some image classification tasks. After presenting how the algorithm works, we compare their properties and report the classification results obtained in two different pattern recognition experiments. Moreover, the paper contains a discussion of the obtained results, and of possible future applications of the similarity features.