یادگیری متریک بر اساس فاصله χ2 سریع برای دسته‌بندی داده‌های هیستوگرامی با دسته‌بندی کننده KNN

نوع مقاله: علمی-پژوهشی

نویسندگان

دانشکده مهندسی برق - دانشگاه صنعتی امیرکبیر

چکیده

مقایسه داده‌ها یک مسأله بنیادی و پرکاربرد در یادگیری ماشین است. در دهه گذشته تحقیقات فراوانی در زمینه یادگیری متریک انجام شده است؛ از کاربردهای یادگیری متریک می‌توان به خوشه‌بندی و دسته‌بندی داده‌ها اشاره کرد. در این مقاله یک روش یادگیری متریک مناسب برای استفاده در مسائل بینایی ماشین ارائه می‌شود. اکثر ویژگی‌هایی که در بینایی ماشین استفاده می‌شوند، هیستوگرامی هستند؛ اما روش‌های یادگیری متریک اغلب بر مبنای فاصله ماهالانوبیس طراحی شده‌اند که در ویژگی‌های هیستوگرامی کارایی مناسبی ندارد. در این تحقیق یک روش جدید یادگیری متریک برای داده‌های هیستوگرامی بر مبنای فاصله مربع کای (χ2) اصلاح شده ارائه می‌شود. فاصله χ2 در دسته‌بندی داده‌های هیستوگرامی دارای دقت بالاتری نسبت به فاصله اقلیدسی است، اما هزینه محاسباتی آن نیز بالاتر است. در این مقاله یک رابطه تقریبی برای فاصله χ2 پیشنهاد می‌شود و بخشی از محاسبات را به مرحله استخراج ویژگی (که به‌صورت غیربرخط قابل محاسبه است) منتقل می‌کند؛ به این ترتیب سرعت مقایسه ویژگی‌ها افزایش می‌یابد. آزمایش‌ها بر روی پایگاه‌های داده مختلف نشان می‌دهد که روش یادگیری متریک پیشنهادی دارای دقت بالایی در دسته‌بندی داده‌های هیستوگرامی مختلف نسبت به روش‌های موجود است. همچنین معیار تقریبی برای فاصله χ2، با حفظ دقت، سرعت مقایسه داده‌ها را 2.5 برابر افزایش می‌دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Metric Learning based on Fast χ2 Distance for Histogram Data Classification via KNN Classifier

نویسندگان [English]

  • H. Sadeghi
  • Abolghasem-A. Raie
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
چکیده [English]

Data comparison is a fundamental problem in machine learning research. Since, metric learning has various applications in clustering and classification problems, it is attracted much attention in the last decades. In this paper, an appropriate metric learning method is presented to utilize in machine vision problems. Common features in machine vision are often histogram; however, metric learning methods are usually designed based on Mahalanobis distance which is not applicable in histogram features. In this study, a new metric learning method based on modified chi-squared distance (χ2) for histogram data is presented. In histogram data classification, χ2 distance is more accurate than Euclidean one; however, its computational cost is higher than Euclidean distance. In this paper, a χ2 distance approximated formulation where a part of its computations is moved into the feature extraction step in offline phase is proposed. Consequently, computational cost of feature comparison is reduced. Experiments on different datasets show that the proposed metric learning method is more accurate than the existing ones in histogram data classification. Moreover, the approximated χ2 distance increases feature comparison speed about 2.5 times without loss of accuracy.

کلیدواژه‌ها [English]

  • Metric learning
  • fast chi-squared distance
  • histogram classification
  • KNN classifier
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