کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4945347 1438421 2017 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A survey on learning approaches for Undirected Graphical Models. Application to scene object recognition
ترجمه فارسی عنوان
یک نظرسنجی در مورد روش های یادگیری برای مدل های گرافیکی غیر عمدتا. درخواست به رسمیت شناختن صحنه
کلمات کلیدی
مدل های گرافیکی ناخواسته، زمینه های تصادفی محض، یادگیری پارامترها، آموزش، تشخیص شیء صحنه،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Probabilistic Graphical Models (PGMs) in general, and Undirected Graphical Models (UGMs) in particular, become suitable frameworks to capture and conveniently model the uncertainty inherent in a variety of problems. When applied to real world applications, such as scene object recognition, they turn into a reliable and widespread resorted tool. The effectiveness of UGMs is tight to the particularities of the problem to be solved and, especially, to the chosen learning strategy. This paper presents a review of practical, widely resorted learning approaches for Conditional Random Fields (CRFs), the discriminate variant of UGMs, which is completed with a thorough comparison and experimental analysis in the field of scene object recognition. The chosen application for UGMs is of particular interest given its potential for enhancing the capabilities of cognitive agents. Two state-of-the-art datasets, NYUv2 and Cornell-RGBD, containing intensity and depth imagery from indoor scenes are used for training and testing CRFs. Results regarding success rate, computational burden, and scalability are analyzed, including the benefits of using parallelization techniques for gaining in efficiency.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 83, April 2017, Pages 434-451
نویسندگان
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