Meta-class features for large-scale object categorization on a budget

In this paper we introduce a novel image descriptor enabling accurate object categorization even with linear models. Akin to the popular attribute descriptors, our feature vector comprises the outputs of a set of classifiers evaluated on the image. However, unlike traditional attributes which represent hand-selected object classes and predefined visual properties, our features are learned automatically and correspond to “abstract” categories, which we name metaclasses. Each meta-class is a super-category obtained by grouping a set of object classes such that, collectively, they are easy to distinguish from other sets of categories. By using “learnability” of the meta-classes as criterion for feature generation, we obtain a set of attributes that encode general visual properties shared by multiple object classes and that are effective in describing and recognizing even novel categories, i.e., classes not present in the training set. We demonstrate that simple linear SVMs trai...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Alessandro Bergamo, Lorenzo Torresani
• Keywords: Computer Vision, Cvpr 2012, Feature Vector, Meta Classes, Traditional Attributes