دوره 18، شماره 4 - ( 12-1400 )                   جلد 18 شماره 4 صفحات 68-49 | برگشت به فهرست نسخه ها

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Mohammadi Kashani M, Amiri S H. Scalable Image Annotation by Summarizing Training Samples into Labeled Prototypes. JSDP. 2022; 18 (4) :49-68
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) :68-49

URL: http://jsdp.rcisp.ac.ir/article-1-1046-fa.html


دانشگاه تربیت دبیر شهید رجایی
چکیده:   (399 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
متن کامل [PDF 1267 kb]   (142 دریافت)    
نوع مطالعه: كاربردي | موضوع مقاله: مقالات پردازش تصویر
دریافت: 1398/4/23 | پذیرش: 1399/5/28 | انتشار: 1401/1/1 | انتشار الکترونیک: 1401/1/1

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