کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406780 678111 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Comparative study among three strategies of incorporating spatial structures to ordinal image regression
ترجمه فارسی عنوان
بررسی مقایسه ای در میان سه راهبرد ترکیب ساختارهای فضایی به رگرسیون تصویر مرتب
کلمات کلیدی
رگرسیون خطی، الگوی برداری، الگو ماتریس، ساختار فضایی، فاصله ی اقلیدسی، بیلیاریر
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• Through summary, find three strategies of using image prior spatial information.
• Apply these strategies to establish OR variants for classifying ordinal image data.
• Conduct comprehensive comparisons among the developed novel OR variants.
• Conclude the effectiveness of spatial structure depends on the embedding way.

Images usually have specific spatial structures, and related researches have shown that these structures can contribute to the establishment of more effective classification algorithms for images. So far though there have been many solutions of making use of such spatial structures separately proposed, little attention has been paid to their systematic summary, let their comparative study alone. On the other hand, we find that the existing image-oriented ordinal regression (OR) methods do not utilize such structure information, which motivates us to compensate a comparative study through embedding such spatial structure into ORs. Towards the end, in this paper, we (1) through a summary, find three typical strategies of using image prior spatial information, i.e., structure-embedded Euclidean distance strategy, structure-regularized modeling strategy for classifier learning, and direct manipulation strategy on images without vectorization for image; more importantly, (2) apply these strategies to establish corresponding ORs for classifying data with ordinal characteristic, conduct comprehensive comparisons and give analysis on them under three evaluation criteria. Experimental results on typical ordinal image datasets JAFFE, UMIST and FG-NET show that the latter two strategies can, on the whole, achieve distinct gain in OR performance and while the first one cannot necessarily as expected, which is due to whether the spatial information is directly embedded into the objective function involved or not.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 136, 20 July 2014, Pages 152–161
نویسندگان
, , ,