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HPMug2oMmNrOfxWQHLiEksa6s0hFu9Ox348d7QefarYlaFR5ArkhOwm3Da1pmxmxCtenj1+6luWD#r#n+EPn9L6Ce+9onqnMlT+i ›› 2015, Vol. 31 ›› Issue (3): 60-64.DOI: 10.3969/j.issn.1009-0479.2015.03.012

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Ｉｍｐｒｏｖｅｄ　Ｍｅｔｈｏｄｏｆ　Ｆａｓｔ　Ｉｍａｇｅ　Ｓｅｇｍｅｎｔａｔｉｏｎ　Ｂａｓｅｄ　ｏｎ　Ｒｏｕｇｈ　Ｓｅｔｓ　ａｎｄ　ＦＣＭ

ＺＨＡＮＧ Ｃｈａｏ－ｑｕａｎ１，ＺＨＯＵ　Ｓｈａｏ－ｊｉｎｇ２ａ，ＬＩＡＮＧ　Ｙｉｎｇ２ｂ   

  1. １Ｆａｃｕｌｔｙ　ｏｆ　Ｓｃｉｅｎｃｅ，Ｊｉａｎｇｘｉ　Ｕｎｉｖｅｒｓｉｔｙ　ｏｆ　Ｓｃｉｅｎｃｅ　ａｎｄ　Ｔｅｃｈｎｏｌｏｇｙ，Ｇａｎｚｈｏｕ３４１０００，Ｃｈｉｎａ；２ａＦａｃｕｌｔｙ　ｏｆ　Ｃｏｍｐｕｔｅｒ　Ｉｎｆｏｒｍａｔｉｏｎ，２ｂＨｕｍａｎ　Ｒｅｓｏｕｒｃｅｓ　Ｄｅｐａｒｔｍｅｎｔ，Ｋｕｎｍｉｎｇ　Ｍｅｔａｌｌｕｒｇｙ　Ｃｏｌｌｅｇｅ，Ｋｕｎｍｉｎｇ６５００３３，Ｃｈｉｎａ
  • Received:2015-04-03 Online:2015-07-01 Published:2015-07-01

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Abstract:

Ｉｎ　ｔｈｉｓ　ｐａｐｅｒ，ｔｈｅ　ｃｏｍｐｅｔｉｔｉｖｅｌｅａｒｎｉｎｇ　ｎｅｕｒａｌ　ｎｅｔｗｏｒｋ　ｆｏｒ　ｒｅｆｅｒｅｎｃｅ，ｔｈｒｏｕｇｈ　ｔｈｅ　ｉｎｔｒｏｄｕｃｔｉｏｎ　ｏｆ　ａｎ　ｉｎｈｉｂｉｔｏｒｙ　ｆａｃｔｏｒ，ｔｏ　ｍａｘｉｍｉｚｅ　ｔｈｅ　ｖａｌｕｅ　ｏｆ　ｍｅｍｂｅｒｓｈｉｐ，ｔｈｅ　ｏｔｈｅｒ　ｃｏｒｒｅｓｐｏｎｄｉｎｇ　ｄｅｃｒｅａｓｅｓ　ｔｈｅ　ｖａｌｕｅ　ｏｆ　ｍｅｍｂｅｒｓｈｉｐ，ｉｎ　ｏｒｄｅｒ　ｔｏ　ａｃｈｉｅｖｅ　ｆａｓｔｅｒ　ｃｌａｓｓｉｆｉｃａｔｉｏｎ．Ａｓ　ｔｏ　ｈｉｇｈｌｉｇｈｔ　ｔｈｅ　ｍａｉｎ　ｆａｃｔｏｒｓａｎｄ　ｓｅｃｏｎｄａｒｙ　ｆａｃｔｏｒｓ，ｃａｎ　ｉｎｈｉｂｉｔ　ｔｈｅ　ｐｕｒｐｏｓｅ．Ｔｈｅ　ｉｍｐｒｏｖｅｄ　ｍｅｔｈｏｄ　ｏｆ　ｒｏｕｇｈ　ｓｅｔ　ａｎｄ　ｆａｓｔ　ｉｍａｇｅ　ｓｅｇｍｅｎｔａｔｉｏｎ　ｂａｓｅｄ　ｏｎ　ＦＣＭ　ｗａｓ　ｐｕｔ　ｆｏｒｗａｒｄ．Ｅｘｐｅｒｉｍｅｎｔａｌ　ｒｅｓｕｌｔｓ　ｓｈｏｗ　ｔｈａｔ　ｔｈｅ　ｆａｓｔ　ｉｍａｇｅ　ｓｅｇｍｅｎｔａｔｉｏｎ　ｍｅｔｈｏｄ　ｆｏｒ　ｉｍａｇｅ　ｎｏｉｓｅ　ｐｏｌｌｕｔｉｏｎ　ｈａｓ　ｔｈｅ　ｂｅｔｔｅｒ　ｓｅｇｍｅｎｔａｔｉｏｎ　ｐｅｒｆｏｒｍａｎｃｅ，ｒｏｕｇｈ　ｓｅｔ　ｔｈｅｏｒｙｈａｓ　ａ　ｕｎｉｑｕｅ　ｗａｙ　ｏｆ　ｄｅａｌｉｎｇ　ｗｉｔｈ　ｕｎｃｅｒｔａｉｎ　ｉｎｆｏｒｍａｔｉｏｎ，ｔｈｅ　ａｂｉｌｉｔｙ　ｔｏ　ｅｘｔｒａｃｔ　ｒｅｌｅｖａｎｔ　ｉｎｆｏｒｍａｔｉｏｎ　ａｎｄ　ｅａｓｙ　ｉｎｔｅｇｒａｔｉｏｎ　ｏｆ　ｏｔｈｅｒ　ｉｎｔｅｌｌｉｇｅｎｔ　ｍｅｔｈｏｄｓ．Ｒｏｕｇｈ　ｓｅｔ　ｔｈｅｏｒｙ　ｈａｓ　ａ　ｇｏｏｄ　ａｐｐｌｉｃａｔｉｏｎ　ｐｒｏｓｐｅｃｔ　ｉｎ　ｔｈｅ　ｆｉｅｌｄ　ｏｆｉｍａｇｅ　ｐｒｏｃｅｓｓｉｎｇ．

Key words: ｒｏｕｇｈ　ｓｅｔｓ, ｉｍａｇｅ　ｓｅｇｍｅｎｔａｔｉｏｎ, ｆｕｚｚｙ　ｃｌｕｓｔｅ

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