روشی نوین برای کاهش تغيير روشنايي در تصاویر غيراخلاقي بر پایه شبکه عصبی عمیق فازی
محورهای موضوعی : عمومىساسان کرمی زاده 1 , ابوذر عرب سرخی 2
1 - پژوهشگاه ارتباطات و فناوري اطلاعات(مركز تحقيقات مخابرات ايران) تهران ، ایران
2 - پژوهشگاه ارتباطات و فناوري اطلاعات(مركز تحقيقات مخابرات ايران) تهران ، ایران
کلید واژه: تغييرات روشنايي, طبقه‌بندي رنگ پوست, تغيير چهره, تصاوير اخلاقی و غیراخلاقی, Gaussian-KNN, الگوریتم ماشین بردار پشتیبان,
چکیده مقاله :
در فضای اینترنت شناسایی عکس‌های غیراخلاقی امری ضروری در جهت حفاظت فیزیکی و ذهنی کودکان محسوب می شود؛ یکی از چالش های اصلی در تشخیص تصاوير غیر اخلاقی تغییرات روشنایی و رنگ پوست بدن است. به همین منظور در این مقاله روشی برای حل تغییرات روشنايي و بهبود تشخیص تصاویر غیراخلاقی ارایه شده‌است. در اين مقاله از شبکه عصبی عمیق فازی برای بهبود روشنایی تصاویر غیراخلاقی استفاده شده است. در روش پیشنهادی از مدل یادگیری عمیق xception جهت تقسیم تصویر براساس شدت روشنايي به بخش‌های مختلف بهره گرفته شده است. تقسیم کردن تصویر به قسمت‌های مختلف باعث بهبود تغییرات روشنايي با حفظ جزئیات تصویر و نهایتا شناسایی بهتر تصاویر غیراخلاقی شده است. به علاوه برای طبقه‌بندی رنگ پوست از ترکیب الگوریتم مبتنی بر Gaussian-KNN بهره گرفته شده است که روشی غیرپارامتری برای طبقه‌بندی‌ها و رگرسیون‌ها است؛ و در انتها از الگوریتم ماشین بردار پشتیبان براي طبقه‌بندی تصاویر استفاده شده است. به منظور پیاده سازی و ارزیابی روش پیشنهادی یک مجموعه شامل 33000 تصویر گردآوری شد، نتایج بدست آمده نشان می‌دهد که طرح پیشنهادی با دقت 7/99 درصد تصاویر غیراخلاقی را تشخیص می دهد.
In the era of the Internet, recognition of adult images is important to children's physical and mental protection. It is a challenge to recognize adult images with changes in the illumination and skin color. In this paper, we proposed a new method for solving illumination normalization with skin color classification in the diagnosis of the adult image. In this paper, the deep fuzzy neural network method is utilized to improve the illumination normalization of adult images, which has improved the recognization of adult images is utilized. Using Xception to dividing the images and reduce the illumination variations in each part separately, which makes it possible to reduce the illumination variation in the whole image without losing details. In addition, the advanced color combination algorithm based on Gaussian-KNN algorithm is used for skin color classification, a non-parametric method is used for classifications and regressions. Finally, the SVM algorithm is utilized for image classification. In this paper, 33,000 different types of images are collected from the Internet. The results show that the proposed method of 1/3 has improved the accuracy of the recognization.
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