Using flower pollination algorithm and atomic potential function for shape matching

Visual shape matching has been a hot research topic. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost-effective in the computation process. The optimization process of shape matching can be regarded as a numerical optimization problem, which is disposed by flower pollination algorithm (FPA). This study comprehensively investigates the convergence performances of FPA and the other algorithms in shape matching problem based on APM model. Experimental results of three realistic examples show that FPA is able to provide very competitive results and to outperform the other algorithms.

[1]  Aboul Ella Hassanien,et al.  Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search , 2017, Adv. Data Anal. Classif..

[2]  Belkacem Mahdad,et al.  Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm , 2016, Appl. Soft Comput..

[3]  Yu Liu,et al.  A new bio-inspired optimisation algorithm: Bird Swarm Algorithm , 2016, J. Exp. Theor. Artif. Intell..

[4]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[5]  R. Maini.  Study and Comparison of Various Image Edge Detection Techniques , 2004 .

[6]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[7]  Mingyue Ding,et al.  Non-rigid multi-modal medical image registration by combining L-BFGS-B with cat swarm optimization , 2015, Inf. Sci..

[8]  M. Emre Celebi.  Real-Time Implementation of Order-Statistics Based Directional Filters , 2009, IET Image Process..

[9]  Jean Dickinson Gibbons,et al.  Nonparametric Statistical Inference , 1972, International Encyclopedia of Statistical Science.

[10]  Nhat-Duc Hoang,et al.  Groutability estimation of grouting processes with cement grouts using Differential Flower Pollination Optimized Support Vector Machine , 2016, Appl. Soft Comput..

[11]  Emad Nabil,et al.  A Modified Flower Pollination Algorithm for Global Optimization , 2016, Expert Syst. Appl..

[12]  Dalia Yousri,et al.  Flower Pollination Algorithm based solar PV parameter estimation , 2015 .

[13]  Günther Greiner,et al.  Interactive partial 3D shape matching with geometric distance optimization , 2014, The Visual Computer.

[14]  Nidhi Chandrakar,et al.  Study and comparison of various image edge detection techniques , 2012 .

[15]  Bijay Ketan Panigrahi,et al.  Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch , 2015 .

[16]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  Faruq Mohammad,et al.  Feature decision-making ant colony optimization system for an automated recognition of plant species , 2015, Expert Syst. Appl..

[18]  Xin-She Yang,et al.  EEG-based person identification through Binary Flower Pollination Algorithm , 2016, Expert Syst. Appl..

[19]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[20]  Samil Temel,et al.  Opportunities and Challenges of Terrain Aided Navigation Systems for Aerial Surveillance by Unmanned Aerial Vehicles , 2014 .

[21]  Bai Li.  Atomic potential matching: An evolutionary target recognition approach based on edge features , 2016 .

[22]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[23]  Bai Li,et al.  An edge-based optimization method for shape recognition using atomic potential function , 2014, Eng. Appl. Artif. Intell..

[24]  Amr Badr,et al.  A binary clonal flower pollination algorithm for feature selection , 2016, Pattern Recognit. Lett..

[25]  Ali Motie Nasrabadi,et al.  Combining two visual cortex models for robust face recognition , 2015 .

[26]  Rui Wang,et al.  Elite opposition-based flower pollination algorithm , 2016, Neurocomputing.

[27]  D. Wolfe,et al.  Nonparametric Statistical Methods. , 1974 .

[28]  Jeng-Shyang Pan,et al.  Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization , 2016, ACIIDS.

[29]  Bai Li,et al.  Shape Matching Optimization via Atomic Potential Function and Artificial Bee Colony Algorithms with Various Search Strategies , 2015, 2015 8th International Symposium on Computational Intelligence and Design (ISCID).

[30]  Yuxiang Zhou,et al.  Local Greedy Flower Pollination Algorithm for Solving Planar Graph Coloring Problem , 2015 .

[31]  Ya Li,et al.  A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching , 2014, TheScientificWorldJournal.

[32]  Ernst D. Dickmanns,et al.  An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles , 1990, IEEE Trans. Syst. Man Cybern..

[33]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[34]  Rui Wang,et al.  Flower Pollination Algorithm with Bee Pollinator for cluster analysis , 2016, Inf. Process. Lett..

[35]  Haibin Duan,et al.  Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft , 2010, Pattern Recognit. Lett..

[36]  Antonio Fernández-Caballero,et al.  A survey of video datasets for human action and activity recognition , 2013, Comput. Vis. Image Underst..

[37]  Ronen Basri,et al.  A Linear Elastic Force Optimization Model for Shape Matching , 2014, Journal of Mathematical Imaging and Vision.

[38]  Amer Draa,et al.  On the performances of the flower pollination algorithm - Qualitative and quantitative analyses , 2015, Appl. Soft Comput..

[39]  R. Mantegna,et al.  Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[40]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).