A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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International Journal of Computer Vision
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This paper deals, for the first time, with an analysis of localization capabilities of weakly supervised categorization systems. Most existing categorization approaches have been tested on databases, which (a) either show the object(s) of interest in a very prominent way so that their localization can hardly be judged from these experiments, or (b) at least the learning procedure was done with supervision, which forces the system to learn only object relevant data. These approaches cannot be directly compared to a nearly unsupervised method. The main contribution of our paper thus is twofold: First, we have set up a new database which is sufficiently complex, balanced with respect to background, and includes localization ground truth. Second, we show, how our successful approach for generic object recognition [14] can be extended to perform localization, too.To analyze its localization potential, we develop localization measures which focus on approaches based on Boosting [5]. Our experiments show that localization depends on the object category, as well as on the type of the local descriptor.