An approach based on simulated annealing to optimize the performance of extraction of the flower region using mean-shift segmentation
Applied Soft Computing, ISSN: 1568-4946, Vol: 13, Issue: 12, Page: 4763-4785
2013
- 11Citations
- 18Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1568494613002652; http://dx.doi.org/10.1016/j.asoc.2013.07.019; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84886728363&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1568494613002652; https://dx.doi.org/10.1016/j.asoc.2013.07.019
Elsevier BV
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