Open AccessAn improved segmentation technique for multilevel thresholding of crop image using cuckoo search algorithm based on recursive minimum cross entropy
Author(s) -
Kumar Arun,
Kumar Anil,
Vishwakarma Amit,
Lee HeungNo
Publication year - 2022
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12148
Subject(s) - cuckoo search , thresholding , entropy (arrow of time) , image segmentation , algorithm , cross entropy , segmentation , artificial intelligence , pattern recognition (psychology) , computer science , lévy flight , mathematics , computation , mathematical optimization , particle swarm optimization , image (mathematics) , statistics , physics , quantum mechanics , random walk
Crop image segmentation is widely used for the analysis of crops. A wide variety of crops are present in the agriculture field, which varies in intensity and complex backgrounds. The thresholding method based on entropy is quite popular for the segmentation of an image. Among all, minimum cross entropy has been widely used. However, the complexity of computation increases when it is used for multilevel thresholding (MLT). Recursive minimum cross entropy is used to resolve the complexity of computation, and cuckoo search (CS) using Levy flight is used to find the optimal threshold for this objective function. Because real‐time applications require less processing time while maintaining high performance, which is validated by the CS algorithm using recursive minimum cross entropy (R‐MCE‐CS) without constraint. The proposed method uses one constraint based on the structural similarity index (SSIM), which leads to an increment in the accuracy for a higher level of thresholding. The accuracy of the proposed method has been tested over 10 crop images with complex backgrounds and high dimensions of colour intensity space. The outcome of the proposed technique has been compared with five algorithms such as wind‐driven optimisation (WDO), bacterial foraging optimisation (BFO), differential evolution (DE), artificial bee colony (ABC), and firefly algorithm (FFA). The result shows that the proposed method gives the most promising result, and the accuracy is also improved.