IEEE Trans Image Process - Incremental training of a detector using online sparse eigendecomposition.

Tópicos

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Resumo

The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently, offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In addition, once learned, an offline detector cannot make use of newly arriving data. To alleviate these drawbacks, online learning has been adopted with the following objectives: 1) the technique should be computationally and storage efficient; 2) the updated classifier must maintain its high classification accuracy. In this paper, we propose an effective and efficient framework for learning an adaptive online greedy sparse linear discriminant analysis model. Unlike many existing online boosting detectors, which usually apply exponential or logistic loss, our online algorithm makes use of linear discriminant analysis' learning criterion that not only aims to maximize the class-separation criterion but also incorporates the asymmetrical property of training data distributions. We provide a better alternative for online boosting algorithms in the context of training a visual object detector. We demonstrate the robustness and efficiency of our methods on handwritten digit and face data sets. Our results confirm that object detection tasks benefit significantly when trained in an online manner.

Resumo Limpo

abil effici accur detect object play crucial role mani comput vision task recent offlin object detector shown tremend success howev one major drawback offlin techniqu complet set train data collect beforehand addit learn offlin detector make use newli arriv data allevi drawback onlin learn adopt follow object techniqu comput storag effici updat classifi must maintain high classif accuraci paper propos effect effici framework learn adapt onlin greedi spars linear discrimin analysi model unlik mani exist onlin boost detector usual appli exponenti logist loss onlin algorithm make use linear discrimin analysi learn criterion aim maxim classsepar criterion also incorpor asymmetr properti train data distribut provid better altern onlin boost algorithm context train visual object detector demonstr robust effici method handwritten digit face data set result confirm object detect task benefit signific train onlin manner

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