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Mohammadi Kashani M, Amiri S H. Scalable Image Annotation by Summarizing Training Samples into Labeled Prototypes. JSDP 2022; 18 (4) : 4
URL: http://jsdp.rcisp.ac.ir/article-1-1046-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-1046-fa.html
محمدی کاشانی محیا، امیری سید حمید. برچسبزنی مقیاس‌پذیر تصاویر با خلاصه‌سازی نمونه‌ها به نماینده‌های برچسب‌دار. پردازش علائم و داده‌ها. 1400; 18 (4) :49-68
دانشگاه تربیت دبیر شهید رجایی
چکیده: (1721 مشاهده)
با افزایش روز‌افزون تصاویر، اندیس‌گذاری و جستجوی سریع آنها در پایگاه داده‌های بزرگ، یک امر ضروری است. یکی از راه‌کارهای مؤثر، نسبت‌دادن یک یا چند برچسب به هر تصویر با هدف توصیف محتوای درون آن است. با وجود کارایی روش‌های خودکار برچسبزنی، یکی از چالش‌های اساسی آنها مقیاس‌پذیری با افزایش تصاویر پایگاه داده است. در این مقاله، با هدف حل این چالش، ابتدا براساس توصیف‌گر بصری تصاویر که از شبکه‌های یادگیری عمیق استخراج می‌شوند، نمایندگان مناسبی به‌دست می‌آیند. سپس، با استفاده از رویه انتشار برچسب بر روی گراف، برچسب‌های معنایی از تصاویر آموزشی به نمایندگان منتشر می‌شوند. با این راه‌کار، به یک مجموعه نمایندگان برچسب‌دار دست خواهیم یافت که می‌توان عمل برچسبزنی هر تصویر آزمون را بر اساس این نمایندگان انجام داد. برای برچسبزنی، یک رویکرد مبتنی بر آستانه‌گذاری وفقی پیشنهاد شده است. با روش پیشنهادی، می‌توان اندازه مجموعه‌داده آموزشی را به 6/22 درصد اندازه اولیه کاهش داد که منجر به تسریع حداقل 2/4 برابری زمان برچسبزنی خواهد شد. همچنین، کارایی برچسبزنی بر روی مجموعه‌داده‌های مختلف برحسب سه معیار دقت، یادآوری و F1 در حد مطلوبی حفظ شده است.
شماره‌ی مقاله: 4
نوع مطالعه: كاربردي |
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مقالات پردازش تصویر
دریافت: 1398/4/23 | پذیرش: 1399/5/28 | انتشار: 1401/1/1 | انتشار الکترونیک: 1401/1/1
دریافت: 1398/4/23 | پذیرش: 1399/5/28 | انتشار: 1401/1/1 | انتشار الکترونیک: 1401/1/1
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