کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
527882 869405 2011 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Boosting part-sense multi-feature learners toward effective object detection
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Boosting part-sense multi-feature learners toward effective object detection
چکیده انگلیسی

AdaBoost has been applied to object detection to construct the detectors with high performance of discrimination and generalization by single-feature learner. However, the poor discriminative power of extremely weak single-feature learners limits its application for general object detection. In this paper, we propose a novel comprehensive learner design mechanism toward effective object detection in terms of both discrimination and generalization abilities. Firstly, the part-sense multi-feature learners are designed to linearly combine the multiple local features to improve the descriptive and discriminative capacity of the learner. Secondly, we formulate the feature selection in part-sense multi-feature learner as a weighted LASSO regression. Using Least Angle Regression (LARS) method, our approach can choose features adaptively, efficiently and as few as possible to guarantee generalization performance. Finally, a robust L1-regularized gradient boosting is proposed to integrate our part-sense sparse features learner into an object classifier. Extensive experiments and comparisons on the face dataset and the human dataset show the proposed approach outperforms the traditional single-feature learner and other multi-feature learners in discriminative and generalization abilities.

Research highlights
► Part-sense multi-feature learners are designed to improve the ability of learners.
► We formulate the feature selection as a weighted LASSO regression.
► A robust L1-regularized gradient boosting is proposed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 115, Issue 3, March 2011, Pages 364–374
نویسندگان
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