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Object recognition is one of the most popular tasks in computer vision. Various works have been reported in recent years. Bag of visual words (BOVW) is shown to be an effective model for this task. BOVW extracts features or visual words from the whole image. However, we know there are two kinds of information in an image: objects and background. Obviously, the features in the objects and background...
A large number of training samples is requiredin developing visual object recognition systems. However, the size of samples is limited sometimes. This paper investigates bagging of one class support vector machines (OCSVM), which just use one class of objects for training. Experiments are performed on Caltech101 database. Our findings show that the performance with bagging method is better than single...
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