Segment-based image classifcaton using Layered-SOM
Andrea Kutics, Christian O’Connell, Akihiko Nakagawa · 2013
Arbitrary domains represent one of the most difficult areas for image classification algorithms to categorize effectively. Inconsistent features require a computationally expensive multipartite approach to search for possible underlying structures within datasets. This paper proposes a new approach to the problem by applying a self-developed, non-linear, multi-scale image segmentation method to identify and extract prominent regions among several visual features expressing color, texture and layout properties. Integrating this method with the Layered Self-Organizing Map has achieved a simple yet powerful multifaceted Artificial Neural Network classifier for mixed domains which has improved abstract classification precision when compared against unsegmented classification methods.