Showing papers by "Ishwar K. Sethi published in 2005"
TL;DR: The experimental results using computer-generated data show that the proposed online learning algorithm can quickly learn the underlying structure from data and clearly show the efficacy of the proposed image segmentation method.
Abstract: In this paper we present a new online learning and classification algorithm and suggest its use for image segmentation. Our learning algorithm follows a variation of Bayesian estimation procedure, which combines prior knowledge and knowledge learned from data. Our classification algorithm strictly follows a statistical classification procedure. The new online learning algorithm is simple to implement, robust to initial parameters and has a linear complexity. The experimental results using computer generated data show that the proposed online learning algorithm can quickly learn the underlying structure from data.
The proposed online learning algorithm is used to develop a novel image segmentation procedure. This image segmentation procedure is based on the region growing and merging approach. First, region growing is carried out using the online learning algorithm. Then, a merging operation is performed to merge the small regions. Two merging methods are proposed. The first method is based on statistical similarity and merges the statistically similar and spatially adjacent regions. The second method uses an information-based approach merging small regions into their neighbouring larger regions. Many experimental results clearly show the efficacy of the proposed image segmentation method.
3 citations
TL;DR: An efficient similarity search system PathSOM that combines Self-Organizing Map (SOM) and Pathfinder Networks (PFNET) and organizes the SOM map units in the form of a graph to yield a framework for an improved search to find the best matching map unit.
Abstract: In this paper, we propose an efficient similarity search system PathSOM that combines Self-Organizing Map (SOM) and Pathfinder Networks (PFNET). In the front end of the system, SOM is applied to cluster the original data vectors and construct a visual map of the data. The Pathfinder network then organizes the SOM map units in the form of a graph to yield a framework for an improved search to find the best matching map unit. The ability of PathSOM approach for efficient searches is demonstrated through well-known data sets.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.