Lessons to learn from a mistaken optimization
• FLICM was proposed as robust image segmentation algorithm derived from fuzzy c-means. • A deep insight is given, into the theoretical foundation of FLICM algorithm. • Several critical issues of FLICM’s objective function are revealed. • FLICM is identified as a non-optimal clustering algorithm rel...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Szilágyi, László [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2014 |
|---|
| Schlagwörter: |
Grouped coordinate minimization |
|---|
| Umfang: |
7 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Thermal structure optimization of a supercondcuting cavity vertical test cryostat - Jin, Shufeng ELSEVIER, 2019, an official publ. of the International Association for Pattern Recognition, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:36 ; year:2014 ; day:15 ; month:01 ; pages:29-35 ; extent:7 |
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| DOI / URN: |
10.1016/j.patrec.2013.08.027 |
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ELV022436103 |
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| 520 | |a • FLICM was proposed as robust image segmentation algorithm derived from fuzzy c-means. • A deep insight is given, into the theoretical foundation of FLICM algorithm. • Several critical issues of FLICM’s objective function are revealed. • FLICM is identified as a non-optimal clustering algorithm related to suppressed FCM. • Solutions are proposed to improve FLICM’s accuracy in image segmentation. | ||
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• FLICM was proposed as robust image segmentation algorithm derived from fuzzy c-means. • A deep insight is given, into the theoretical foundation of FLICM algorithm. • Several critical issues of FLICM’s objective function are revealed. • FLICM is identified as a non-optimal clustering algorithm related to suppressed FCM. • Solutions are proposed to improve FLICM’s accuracy in image segmentation. |
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• FLICM was proposed as robust image segmentation algorithm derived from fuzzy c-means. • A deep insight is given, into the theoretical foundation of FLICM algorithm. • Several critical issues of FLICM’s objective function are revealed. • FLICM is identified as a non-optimal clustering algorithm related to suppressed FCM. • Solutions are proposed to improve FLICM’s accuracy in image segmentation. |
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• FLICM was proposed as robust image segmentation algorithm derived from fuzzy c-means. • A deep insight is given, into the theoretical foundation of FLICM algorithm. • Several critical issues of FLICM’s objective function are revealed. • FLICM is identified as a non-optimal clustering algorithm related to suppressed FCM. • Solutions are proposed to improve FLICM’s accuracy in image segmentation. |
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