کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
535721 870369 2013 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Ensembles of strong learners for multi-cue classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Ensembles of strong learners for multi-cue classification
چکیده انگلیسی

Real world heterogeneous scenes contain objects of a large variety of forms, surfaces, colors and textures, thus multi-modal approaches are needed to deal with their challenges. A promising method of combining various sources of information are ensemble methods which allow on the fly integration of classification modules, specific to a single sensor modality, into a classification process. These modular and extensible approaches have the advantage that they do not require that a single method copes with every eventuality, but combine existing specialized methods to overcome their weaknesses.In addition, the rapid growth of the perception field means that comparing, evaluating, sharing and combining the available approaches becomes increasingly relevant. In this article we describe a novel training strategy for ensembles of strong learners that not only outperform the best member but also the best classifier trained on the concatenation of features.The method was evaluated using a large RGBD dataset containing Kinect scans of 300 objects and special use-cases are presented that highlight how ensemble learning can be used to improve classification results.


► Classification ensembles based on strong learners to improve on concatenations.
► Multi-modal perception for object recognition.
► Comparing different features and classifiers, testing their scalability.
► Modular, multi-language and portable framework for distributed/robotic systems.
► Open-source code running on a public database for evaluation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 34, Issue 7, 1 May 2013, Pages 754–761
نویسندگان
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