A Survey on Segmentation Techniques in Fractal Classification Systems

Fractal-based classification systems have gained significant attention in recent years due to their ability to model complex data structures efficiently. These systems rely on the segmentation of data into self-replicating patterns, which are integral to their classification process. This survey explores the various segmentation techniques such as image pyramids, wavelet transform, region growing, k-means clustering, and fractal dimension-based segmentation employed in fractal classification systems, providing an in-depth analysis of their strengths, weaknesses, and applications. Each technique is evaluated based on its suitability for different data types and domains, highlighting their advantages and limitations. Finally, the survey outlines the current challenges and future directions in the field of fractal-based classification systems, emphasizing the need for improved segmentation techniques to handle large and complex datasets. By providing a comprehensive overview of segmentation methods in the context of fractal classification, this survey aims to contribute insights for improved pattern recognition in diverse applications.