Pre-informed level set for flower image segmentation

Abstract

This work proposes a pre-informed Chan–Vese (CV) based level sets algorithm. Pre-information includes objects colour, texture and shape fused features. The aim is to use this algorithm to segment flower images and extract meaningful features that will help is classification of floral content. Shape pre-information modelling is handled manually using advance image processing tools. Local binary patterns (LBP) features makeup texture pre-information and RGB colour channels of the object provide colour pre-information. All pre-defined object information is fused together to for high dimension subspace defining object characteristics. Testing of the algorithm on flower images datasets shows a jump in information content in the resulting segmentation output compared to other models in the category. Segmentation of flowers is important for recognition, classification and quality assessment to ever-increasing volumes in floral markets.