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基于模擬退火算法—支持向量機(jī)的儲(chǔ)糧害蟲識(shí)別分類
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    摘要:

    將模擬退火算法應(yīng)用于糧蟲圖像識(shí)別中支持向量機(jī)分類器參數(shù)C和g的優(yōu)化，并與網(wǎng)格搜索法優(yōu)化結(jié)果進(jìn)行了對(duì)比，結(jié)果表明參數(shù)優(yōu)化速度提高了3.91倍，分類器的識(shí)別率提高了5.56%。應(yīng)用SAA—SVM分類器對(duì)糧倉(cāng)中危害嚴(yán)重的9類糧蟲進(jìn)行了自動(dòng)分類，識(shí)別率達(dá)到95.56%，證實(shí)了基于SAA—SVM的分類器對(duì)糧蟲進(jìn)行自動(dòng)分類是可行的。

    Abstract:

    The design of the classifier is an important part for the image recognition system of the stored-grain pests. The simulated annealing algorithm (SAA) was proposed to optimize parameters C and g in the classifier based on support vector machine (SVM), and it was compared with the grid-search optimization. The results indicated that the optimizing efficiency was improved about 3.91 times, and the recognition ratio of the SVM classifier was raised by 5.56%. The nine species of the stored-grain pests in grain-depot were automatically recognized by the classifier based on simulated annealing algorithm and support vector machine, the correct recognition ratio was over 95.56%. The experimental results prove that the method is practical and feasible.  

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胡玉霞,張紅濤.基于模擬退火算法—支持向量機(jī)的儲(chǔ)糧害蟲識(shí)別分類[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2008,39(9):108-111.[J]. Transactions of the Chinese Society for Agricultural Machinery,2008,39(9):108-111.

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