Convergence of an annealing algorithm
Mathematical Programming: Series A and B
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Automatic Recognition of Blooming Flowers
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI
Expert Systems with Applications: An International Journal
Introduction to Information Retrieval
Introduction to Information Retrieval
Mean-shift-based color segmentation of images containing green vegetation
Computers and Electronics in Agriculture
A spatially distributed model for foreground segmentation
Image and Vision Computing
Engineering Applications of Artificial Intelligence
Delving deeper into the whorl of flower segmentation
Image and Vision Computing
AUTOMATIC FLOWER BOUNDARY EXTRACTION USING IPSOAntK-MEANS ALGORITHM
Cybernetics and Systems
A simulated annealing algorithm for solving the bi-objective facility layout problem
Expert Systems with Applications: An International Journal
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simulated annealing method based on a specialised evolutionary algorithm
Applied Soft Computing
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking
IEEE Transactions on Image Processing
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
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Flower identification and recognition are tedious and difficult tasks even for humans. Image segmentation based on automatic flower extraction is an essential step for computer-aided flower image recognition and retrieval processes. Furthermore, there is a challenge for segmentation of the object(s) from natural complex background in color images. In this study, a novel performance optimization approach for image segmentation, i.e. simulated annealing-based mean-shift segmentation (SAMS), is proposed and implemented. It is based on the simulated annealing solution of quadratic assignment problem model treated as an image segmentation process using feature-based mean-shift (MS) clustering on color images. The proposed approach is designed to realize a global and unsupervised (i.e., fully automatic) segmentation. It is a modified and optimized version of Backprojection-based mean-shift segmentation (BackMS) method. In conducted segmentation experiments, the performance results of SAMS approach are compared with the ones of BackMS method. Comparison of overall performance results and statistical analysis (i.e., Wilcoxon signed rank median test) show that SAMS approach improves the performance of BackMS method. It is measured as 49.33% when using object bounding boxes and as 51.33% when using object pixel regions.