Support Vector Data Description for image categorization from Internet images

Training a classifier for object category recognition using images on the Internet is an attractive approach due to its scalability. However, a big challenge in this approach is that it is difficult to automatically obtain sets of negative samples that are guaranteed to be free of positive samples. In this paper we propose to address this challenge with a Support Vector Data Description (SVDD) classifier. An SVDD classifier does not need negative images in training. It computes a hypersphere around the potentially good images in the feature space and uses this boundary to distinguish images of target visual category from outliers. Evaluation on standard test sets shows that we are able to achieve competitive classification performance using the contaminated training images from the Internet without the need for large datasets of negative examples.