کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6939654 1449972 2018 41 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks
چکیده انگلیسی
Performing effective image retrieval tasks, capable of exploiting the underlying structure of datasets still constitutes a challenge research scenario. This paper proposes a novel manifold learning approach that exploits the intrinsic dataset geometry for improving the effectiveness of image retrieval tasks. The underlying dataset manifold is modeled and analyzed in terms of a Reciprocal kNN Graph and its Connected Components. The method computes the new retrieval results on an unsupervised way, without the need of any user intervention. A large experimental evaluation was conducted, considering different image retrieval tasks, various datasets and features. The proposed method yields better effectiveness results than various methods recently proposed, achieving effectiveness gains up to +40.75%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 75, March 2018, Pages 161-174
نویسندگان
, , ,