روش هیبریدی بهینه سازی ازدحام ذرات کوانتومی و گرگ خاکستری جهت آنالیز خوشه بهینه به منظور بخش‌بندی پوست چهره

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار دانشکده فنی و مهندسی، گروه برق و کامپیوتر، دانشگاه محقق اردبیلی، اردبیل، ایران

2 دانشجوی دکتری دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

3 استادیار گروه مهندسی برق، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران

4 دانشیار دانشکده پزشکی و پیراپزشکی، گروه درماتولوژی، دانشگاه علوم پزشکی اردبیل، اردبیل، ایران

چکیده

بخش‌بندی چهره نقش خیلی مهمی در کاربردهای آنالیز چهره مانند شناسایی هویت، آنالیز حالات چهره، انیمیشن چهره و آنالیز بیماری‌های پوست چهره ایفا می‌کند. در این مقاله، یک روش جدید هیبریدی مبتنی بر بهینه‌سازی ازدحام ذرات کوانتومی (QPSO) و گرگ خاکستری (GWO) جهت بهینه کردن عمل‌کرد خوشه‌بند K-Means رائه می‌شود. در این مطالعه، دو الگوریتم با هم ترکیب شده و در روش پیشنهادی عمل‌کرد استخراج در الگوریتم QPSO با قابلیت اکتشاف در الگوریتم GWO بهبود داده می‌شود. اندازه‌گیری تشابه نقش اساسی در فرآیند خوشه‌بندی ایفا می‌کند. جهت اندازه‌گیری تشابه، 4 معیار فاصله‌ی اقلیدسی، مینکوفسکی، ماهالانوبیس و بلوک شهری در بهینه‌سازی الگوریتم K-Means به-کار گرفته شده است. روش پیشنهادی در مقایسه با سایر الگوریتم‌های فرا ابتکاری شامل الگوریتم ژنتیک (GA)، PSO، QPSO، GWO، بهینه‌سازی خفاش، جستجوی کلاغ عمل‌کرد بهتری در بخش‌بندی و سرعت همگرایی دارد. همچنین، نتایج نشان می‌دهند که فاصله‌ی مینکوفسکی عمل‌کرد بهتری در محاسبه‌ی تشابه داشته و بهینه‌سازی الگوریتم K-Means با فاصله‌ی مینکوفسکی نتیجه‌ی بهتری در بخش‌بندی دارد. براساس نتایج به‌دست آمده، ترکیب این دو الگوریتم رسیدن به جواب بهینه را تضمین کرده و از مسئله‌ی کمینه مکانی نیز جلوگیری می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

Hybrid Algorithm of Quantum Particle Swarm Optimization and Grey Wolf Optimization for Optimum Cluster Analysis Applicable for Facial Skin Segmentation

نویسندگان [English]

  • Mehdi Nooshyar 1
  • Ali Fahmi Jafargholkhanloo 2
  • Mohammad Ghiamy 3
  • Majid Rostami Mogaddam 4
1 Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
3 Department of Electrical Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
4 Department of Dermatology, Imam Reza Hospital, Ardabil University of Medical Sciences, Ardabil, Iran
چکیده [English]

Facial skin segmentation plays an important role in applications such as identification, facial expression analysis, facial animation, and skin disease analysis. Clustering is one of the most common methods for image segmentation. In this paper, a new hybrid method based on Quantum Particle Swarm Optimization and Grey Wolf Optimization is presented to optimize the performance of the K-Means clustering. By Combination of two algorithms, the exploitation performance of the QPSO algorithm is improved by the exploration capability of the GWO algorithm. To measure the similarity, four distance criteria including Euclidean, Minkowski,  Mahalanobis, and City-Block distances have been used to optimize the K-Means algorithm. The proposed method has a better performance in segmentation and convergence speed compared to other meta-heuristic algorithms such as Genetic Algorithm, GWO, PSO, QPSO, Bat Optimization, Crow Search Algorithm. The experimental results show that Minkowski distance has a better performance in calculating similarity and optimization of K-Means algorithm. Based on the obtained results, the proposed method ensures the achievement of the optimal solution and prevents the problem from falling to a local minimum.

کلیدواژه‌ها [English]

  • Distance Criterion Types
  • Facial Skin Segmentation
  • Quantum Particle Swarm Optimization
  • Gray Wolf Optimization
  • Facial Color Image

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 1، شماره 1
فروردین 1402
صفحه 116-128

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