انطباق دامنه کرنلی و تطبیق توزیع متعادل برای طبقه‌بندی تصاویر

نوع مقاله : مقاله پژوهشی فارسی

نویسندگان

1 دانشکده مهندسی فناوری اطلاعات و کامپیوتر، دانشگاه صنعتی ارومیه، ارومیه، ایران.

2 گروه کامپیوتر، دانشگاه صنعتی ارومیه، ارومیه، ایران

چکیده

یادگیری انتقالی و انطباق دامنه از جمله راه حل‌های موثر در بهبود عملکرد طبقه‌بند‌های تصویر هستند که در آن دامنه منبع (مجموعه آموزشی) و دامنه هدف (مجموعه آزمایشی) از اختلاف توزیع احتمال قابل توجهی برخوردارند. در واقع، نظر به اینکه جمع‌آوری داده های ورودی در شرایط مختلف (مانند وضعیت نور یا درجه حرارت)، تجهیزات مختلف با ویژگی های متغیر (مانند تعداد پورت‌های ورودی یا کیفیت رزولوشن) و دیدگاه‌های مختلف (مانند ابعاد و محیط) انجام می‌شود، منجر به مسئله‌ی تغییر دامنه می‌شود. انطباق دامنه نیمه نظارت شده، راه حلی پیشتاز برای مسئله‌ی تغییر دامنه است که در آن، دامنه منبع و بخش کوچکی از دامنه هدف دارای برچسب هستند. در این مقاله انطباق دامنه کرنلی و تطبیق توزیع متعادل (KEDA) را برای انطباق دامنه‌های منبع و هدف، به صورت نیمه نظارتی پیشنهاد می‌کنیم. KEDA توپولوژی دامنه‌ها را از طریق ایجاد ماتریس لاپلاسی و از نقطه نظرهای شباهت‌ و تفاوت‌ حفظ می‌کند. علاوه بر این، KEDA توزیع شرطی و حاشیه‌ای بین دامنه‌ها را تطبیق می دهد. در نهایت، مجموع این راه حل‌ها، تابع طبقه‌بندی خوبی برای برچسب زدن تصاویر بدون برچسب نتیجه می‌دهد. روش پیشنهادی با روش‌های پیشرفته انطباق دامنه بر روی دیتاست‌های آفیس-کلتک-10 ، اعداد ، پای و کویل مقایسه شده است که نتایج، بهبود عملکرد قابل توجهی در روش پیشنهادی ما را نشان می‌دهد.

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