Abstract
We propose an incremental machine-learning approach for object recognition where new images are continuously added and the recognition decision is made with no delay. First, the object region is automatically represented using a bag of covariance features. Then an on-line variant of the random forest (RF) classifier is employed to select object descriptors and to learn the object classifiers. A validation of the method by empirical studies in the domain of the GRAZ02 dataset shows its superior performance over those methods which are histogram-based, and subsequently yields in object recognition performance comparable to that of state-of-the-art classifiers.
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This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009
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Osman, H.E. Covariance-based recognition using an incremental learning approach. Artif Life Robotics 14, 233–236 (2009). https://doi.org/10.1007/s10015-009-0660-7
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DOI: https://doi.org/10.1007/s10015-009-0660-7