دانلود مقاله ISI انگلیسی شماره 46216
عنوان فارسی مقاله

تقسیم بندی تصویر رنگ بر اساس بهینه سازی کلونی زنبور عسل مصنوعی چندهدفه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46216 2015 13 صفحه PDF سفارش دهید 10000 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Color image segmentation based on multiobjective artificial bee colony optimization
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Applied Soft Computing, Volume 34, September 2015, Pages 389–401

کلمات کلیدی
تقسیم بندی تصویر رنگ - بهینه سازی چندمنظوره - کلنی زنبور عسل مصنوعی -
پیش نمایش مقاله
پیش نمایش مقاله تقسیم بندی تصویر رنگ بر اساس بهینه سازی کلونی زنبور عسل مصنوعی چندهدفه

چکیده انگلیسی

This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization.

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