-استخراج ویژگی با کمک تجزیه به عناصر تنک به منظور طبقه بندی چهره

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، دانشکده برق و کامپیوتر، دانشگاه صنعتی همدان، همدان رایانامه: h.doosti@hut.ac.ir

چکیده

 در سـالهای اخیر، اسـتخراج ویژگی بهعنوان یک مرحله میانی در طبقهبندی موردتوجه پژوهشـگران بودهاسـت.
در این مقاله، یک روش نوین بهمنظور اســتخراج ویژگی بانظارت با کمک تجزیه به عناصــر تنک پیشــنهاد میشــود.
الگوریتم پیشـنهادی شـامل دو مرحله اسـت که در مرحله اول اطلاعات مشـترک دادهها در یک ماتریس با مرتبه کم قرار
میگیرد و در مرحله دوم یک روش اســتخراج ویژگی خطی مانند نگاشــت حفظ موقعیت مکانی بهمنظور اســتخراج
نهایی ویژگیها مورد اســتفاده قرار میگیرد. ســپس ویژگیهای اســتخراج شــده به طبقهبند ماشــینبردارپشــتیبان داده
میشـود. بهمنظور سـنجش صـحت روش پیشـنهادی، از سـه مجموعه داده اسـتفاده میشـود. نتایج آزمایش نشـاندهنده
برتری روش پیشنهادی نسبت به برخی از روشهای مدرن استخراج ویژگی است 

کلیدواژه‌ها

عنوان مقاله [English]

Feature extraction using sparse component decomposition for face classification

نویسنده [English]

  • Hamid Reza Shahdoosti
Assistance Prof. Electrical and Computer Engineering Department,, Hamedan University of tecnology. Hamedan, Iran. Email: h.doosti@hut.ac.ir
چکیده [English]

In the recent years, the feature extraction as an intermediate step in the classification, has attracted the attention of researchers. In this paper, a new supervised feature extraction method is proposed using sparse component decomposition. The proposed algorithm has two steps.In the first step, the common information of the data matrix is extracted in a low rank matrix. In he second step, a linear feature extractor method such as local preservation projection one is used to extract the final features. Then, the extracted features are fed to the support vector machine classifier. To evaluate the accuracy rate of the proposed method, three datasets are used. The results show that the proposed method outperforms compared with some state of the art methods.

کلیدواژه‌ها [English]

  • Feature extraction
  • Face classification
  • Sparse decomposition
  • Support vector machine

مراجع

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