کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10369764 | 875577 | 2005 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adaptive relevance feedback based on Bayesian inference for image retrieval
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Relevance feedback can be considered as a Bayesian classification problem. For retrieving images efficiently, an adaptive relevance feedback approach based on the Bayesian inference, rich get richer (RGR), is proposed. If the feedback images in current iteration are consistent with the previous ones, the images that are similar to the query target are assigned to high probabilities. Therefore, the images that are similar to the user's ideal target are emphasized step by step. The experiments showed that the average precision of RGR improves 5-20% on each interaction compared with non-RGR. When compared with MARS, the proposed approach greatly reduces the user's efforts for composing a query and captures user's intention efficiently.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 85, Issue 2, February 2005, Pages 395-399
Journal: Signal Processing - Volume 85, Issue 2, February 2005, Pages 395-399
نویسندگان
Lijuan Duan, Wen Gao, Wei Zeng, Debin Zhao,