• Modified artificial bee colony...

Treffer: Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions

Title:
Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions
Authors:
BHANDARI, A. K, KUMAR, A, SINGH, G. K
Source:
Expert systems with applications. 42(3):1573-1601
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 1 p.1/4
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Terre, ocean, espace, Earth, ocean, space, Geophysique externe, External geophysics, Télédétection, photointerprétation, Remote sensing, photointerpretation, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme recherche, Search algorithm, Algoritmo búsqueda, Apprentissage renforcé, Reinforcement learning, Aprendizaje reforzado, Chaos, Caos, Détection seuil, Threshold detection, Detección umbral, Entropie, Entropy, Entropía, Fonction objectif, Objective function, Función objetivo, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Intelligence en essaim, Swarm intelligence, Inteligencia de enjambre, Modélisation, Modeling, Modelización, Optimisation PSO, Particle swarm optimization, Optimización PSO, Optimum global, Global optimum, Optimo global, Résultat expérimental, Experimental result, Resultado experimental, Seuil, Threshold, Umbral, Temps minimal, Minimum time, Tiempo mínimo, Traitement image, Image processing, Procesamiento imagen, Télédétection spatiale, Space remote sensing, Teledetección espacial, Variance, Variancia, Vision ordinateur, Computer vision, Visión ordenador, Apprentissage par opposition, Opposition-based learning, Aprendizaje por Oposición, Optimisation par colonies d'abeilles, Bee colony optimization, Algoritmo de enjambre de Abejas, Segmentation image, Image segmentation, Segmentación de imágenes, Vie artificielle, Artificial life, Vida artificial, ABC, Between-class variance, Kapur's entropy, MABC, Multilevel thresholding, PSO and GA algorithm, Tsallis entropy
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482005, India
Department of Electrical Engineering, Indian Institute Technology Roorkee, Uttarakhand 247667, India
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28928475
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

External geophysics
Accession Number:
edscal.28928475
Database:
PASCAL Archive

Weitere Informationen

In this paper, a modified artificial bee colony (MABC) algorithm based satellite image segmentation using different objective function has been presented to find the optimal multilevel thresholds. Three different methods are compared with this proposed method such as ABC, particle swarm optimization (PSO) and genetic algorithm (GA) using Kapur's, Otsu and Tsallis objective function for optimal multilevel thresholding. The experimental results demonstrate that the proposed MABC algorithm based segmentation can efficiently and accurately search multilevel thresholds, which are very close to optimal ones examined by the exhaustive search method. In MABC algorithm, an improved solution search equation is used which is based on the bee's search only around the best solution of previous iteration to improve exploitation. In addition, to improve global convergence when generating initial population, both chaotic system and opposition-based learning method are employed. Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.