scholarly journals A Novel Plant Root Foraging Algorithm for Image Segmentation Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Lianbo Ma ◽  
Kunyuan Hu ◽  
Yunlong Zhu ◽  
Hanning Chen ◽  
Maowei He
Keyword(s):  
Image Segmentation ◽  
Foraging Behavior ◽  
Plant Root ◽  
Biologically Inspired ◽  
Foraging Patterns ◽  
Root Foraging ◽  
New Type ◽  
Optimization Methodology ◽  
Multimodal Problems ◽  
Segmentation Problem

This paper presents a new type of biologically-inspired global optimization methodology for image segmentation based on plant root foraging behavior, namely, artificial root foraging algorithm (ARFO). The essential motive of ARFO is to imitate the significant characteristics of plant root foraging behavior including branching, regrowing, and tropisms for constructing a heuristic algorithm for multidimensional and multimodal problems. A mathematical model is firstly designed to abstract various plant root foraging patterns. Then, the basic process of ARFO algorithm derived in the model is described in details. When tested against ten benchmark functions, ARFO shows the superiority to other state-of-the-art algorithms on several benchmark functions. Further, we employed the ARFO algorithm to deal with multilevel threshold image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the suitability of the proposed method for solving such problem.

scholarly journals Artificial Plant Root System Growth for Distributed Optimization: Models and Emergent Behaviors

2016 ◽  
Vol 11 (1) ◽  
pp. 447-457 ◽  
Author(s):  
Weixing Su ◽  
Lin Na ◽  
Fang Liu ◽  
Wei Liu ◽  
Muhammad Aqeel Ashraf ◽  
...  
Keyword(s):  
Foraging Behavior ◽  
Plant Root ◽  
Root Systems ◽  
Branching Random Walk ◽  
Root Tip ◽  
Root Foraging ◽  
Proposed Model ◽  
Plant Root System ◽  
Optimality Principles ◽  
Emergent Behaviors

AbstractPlant root foraging exhibits complex behaviors analogous to those of animals, including the adaptability to continuous changes in soil environments. In this work, we adapt the optimality principles in the study of plant root foraging behavior to create one possible bio-inspired optimization framework for solving complex engineering problems. This provides us with novel models of plant root foraging behavior and with new methods for global optimization. This framework is instantiated as a new search paradigm, which combines the root tip growth, branching, random walk, and death. We perform a comprehensive simulation to demonstrate that the proposed model accurately reflects the characteristics of natural plant root systems. In order to be able to climb the noise-filled gradients of nutrients in soil, the foraging behaviors of root systems are social and cooperative, and analogous to animal foraging behaviors.

scholarly journals Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

2016 ◽  
Vol 2016 ◽  
pp. 1-16
Author(s):  
Yang Liu ◽  
Junfei Liu ◽  
Liwei Tian ◽  
Lianbo Ma
Keyword(s):  
Image Segmentation ◽  
Iterative Root ◽  
Continuous Space ◽  
Root Foraging ◽  
Computation Efficiency ◽  
Spatial Population ◽  
Complex Image ◽  
Exchange Information ◽  
Comparative Results ◽  
Segmentation Problem

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency.

scholarly journals Foraging behavior and at-sea distribution of White-Tailed Tropicbirds in tropical ocean

2017 ◽  
Vol 78 (3) ◽  
pp. 556-563 ◽  
Author(s):  
L. F. A. S. Campos ◽  
A. B. Andrade ◽  
S. Bertrand ◽  
M. A. Efe
Keyword(s):  
Endangered Species ◽  
Foraging Behavior ◽  
Mortality Rates ◽  
Nest Attendance ◽  
Breeding Colony ◽  
Tropical Ocean ◽  
Foraging Patterns ◽  
Land Crab ◽  
Egg Consumption ◽  
Foraging Pattern

Abstract We used miniaturized GPS loggers and site observations to access foraging patterns and nest behaviour of the White-tailed Tropicbird Phaethon lepturus (WTTB), an endangered species at its South Atlantic breeding colony. Dual foraging pattern was observed with alternation between long and short foraging trips. Birds responsible for nest attendance engaged in short foraging trips with mean distance from colony of 25 ± 17 km, total distance covered of 79 ± 65 km and mean duration of 4.02 ± 5.28 hours. Birds flew by dawn and returned before dusk while partners were at sea for long foraging trips that ranged from four to 11 days, with mean maximum distance from colony of 105 ± 47.48 km. Chicks were usually left alone for hours and chick predation by Land Crab Johngartia lagostroma, egg consumption by Goniopsis cruentata and intra-specific competition are suspected to be responsible for high chick mortality rates.

scholarly journals Modified JSEG algorithm for reducing over-segmentation problems in underwater coral reef images

2019 ◽  
Vol 9 (6) ◽  
pp. 5244
Author(s):  
Mohammad Sameer Aloun ◽  
Muhammad Suzuri Hitam ◽  
Wan NuralJawahir Hj Wan Yussof ◽  
Abdul Aziz K Abdul Hamid ◽  
Zainuddin Bachok
Keyword(s):  
Image Segmentation ◽  
Coral Reef ◽  
Texture Features ◽  
Color Histogram ◽  
Similarity Measurement ◽  
Segmentation Problem ◽  
Better Than ◽  
Observation Results ◽  
Color And Texture Features

<p>The original JSEG algorithm has proved to be very useful and robust in variety of image segmentation case studies.However, when it is applied into the underwater coral reef images, the original JSEG algorithm produces over-segementation problem, thus making this algorithm futile in such a situation. In this paper, an approach to reduce the over-segmentation problem occurred in the underwater coral reef image segmentation is presented. The approach works by replacing the color histogram computation in region merge stage of the original JSEG algorithm with the new computation of color and texture features in the similarity measurement. Based on the perceptual observation results of the test images, the proposed modified JSEG algorithm could automatically segment the regions better than the original JSEG algorithm.</p>

scholarly journals Artificial Bee Colony Optimizer with Bee-to-Bee Communication and Multipopulation Coevolution for Multilevel Threshold Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Jun-yi Li ◽  
Yi-ding Zhao ◽  
Jian-hua Li ◽  
Xiao-jun Liu
Keyword(s):  
Image Segmentation ◽  
Information Exchange ◽  
Artificial Bee Colony ◽  
Experimental Results ◽  
New Method ◽  
Communication Pattern ◽  
Communication Model ◽  
Von Neumann ◽  
Bee Colony ◽  
Segmentation Problem

This paper proposes a modified artificial bee colony optimizer (MABC) by combining bee-to-bee communication pattern and multipopulation cooperative mechanism. In the bee-to-bee communication model, with the enhanced information exchange strategy, individuals can share more information from the elites through the Von Neumann topology. With the multipopulation cooperative mechanism, the hierarchical colony with different topologies can be structured, which can maintain diversity of the whole community. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the advantage of the MABC algorithm. Furthermore, we employed the MABC algorithm to resolve the multilevel image segmentation problem. Experimental results of the new method on a variety of images demonstrated the performance superiority of the proposed algorithm.

Multilevel Thresholding for Image Segmentation Based on Cellular Metaheuristics

2018 ◽  
Vol 9 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Mohamed Abdou Bouteldja ◽  
Mohamed Baadeche ◽  
Mohamed Batouche
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Multilevel Thresholding ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Cellular Genetic Algorithm ◽  
Kapur’S Entropy ◽  
Computationally Expensive ◽  
Segmentation Problem ◽  
Nature Inspired Metaheuristics

This article describes how multilevel thresholding image segmentation is a process used to partition an image into well separated regions. It has various applications such as object recognition, edge detection, and particle counting, etc. However, it is computationally expensive and time consuming. To alleviate these limitations, nature inspired metaheuristics are widely used to reduce the computational complexity of such problem. In this article, three cellular metaheuristics namely cellular genetic algorithm (CGA), cellular particle swarm optimization (CPSO) and cellular differential evolution (CDE) are adapted to solve the multilevel thresholding image segmentation problem. Experiments are conducted on different test images to assess the performance of the cellular algorithms in terms of efficiency, quality and stability based on the between-class variance and Kapur's entropy as objective functions. The experimental results have shown that the proposed cellular algorithms compete with and even outperform existing methods for multilevel thresholding image segmentation.

A novel bionic algorithm inspired by plant root foraging behaviors

2015 ◽  
Vol 37 ◽  
pp. 95-113 ◽  
Author(s):  
Lianbo Ma ◽  
Yunlong Zhu ◽  
Yang Liu ◽  
Liwei Tian ◽  
Hanning Chen
Keyword(s):  
Plant Root ◽  
Root Foraging

Observations on the Flight Patterns and Foraging Behavior of Tasmanian Bats

1986 ◽  
Vol 13 (3) ◽  
pp. 427 ◽  
Author(s):  
MG Oneill ◽  
RJ Taylor
Keyword(s):  
Foraging Behavior ◽  
Foraging Behaviour ◽  
Foraging Patterns ◽  
Broad Agreement ◽  
Direct Flight

Observations were made on the flight patterns and foraging behaviour of Tasmanian bat species, by the use of light tags and the release of individuals at dusk while it was still light enough to see clearly. Four distinct foraging patterns were observed, each being characteristic of a pair of species. These pairs were: Nyctophrlusgeoffroyi and N. timoriensis (slowest flight, undulating, closest to vegetation); Eptesicus vulturnus and E. regulus (highly manoeuvrable, faster than Nyctophilus, further from vegetation); E. sagittula and Chalinolobus morio (fast, direct flight, less manoeuvrable than smaller Eptesicus and fly higher); and Pipistrellus tasmaniensis and C. gouldii (faster, most direct flight, limited manoeuvrability, prefer open areas). There is a broad agreement between the flight patterns observed and the relative shape of the wing of each species.

A Kind of Watershed Image Segmentation Method Based on Combination Pre-Processing

2010 ◽  
Vol 121-122 ◽  
pp. 320-324
Author(s):  
Jin Xi Wang ◽  
Lin Xiang Liu ◽  
Xiu Zheng Li
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Simple Operation ◽  
Processing Steps ◽  
Segmentation Problem ◽  
Image Segmentation Algorithm

The watershed algorithm has been widely used in image segmentation for its characteristics of accurately positioning edge, simple operation and etc. But it also has drawbacks of easy to over-segmentation and loss important outline for the character of sensitive to noise. Aiming at the problem of over-segmentation of watershed algorithm, the paper brought out an improved image segmentation algorithm based on watershed, which can limit the number of existing regions that are allowed with combination pre-processing steps, so that the over-segmentation problem can be better solved. The result of experiment also verifies the correctness and feasibility of the proposed algorithm in the paper.

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