Adapting an Object Detector by Considering the Worst Case: a Conservative Approach

The performance of an offline-trained classifier can be improved on-site by adapting the classifier towards newly acquired data. However, the adaptation rate is a tuning parameter affecting the performance gain substantially. Poor selection of the adaptation rate may worsen the performance of the original classifier. To solve this problem, we propose a conservative model adaptation method by considering the worst case during the adaptation process. We first construct a random cover of the set of the adaptation data from its partition. For each element in the cover (i.e. a portion of the whole adaptation data set), we define the crossentropy error function in the form of logistic regression. The element in the cover with the maximum cross-entropy error corresponds to the worst case in the adaptation. Therefore we can convert the conservative model adaptation into the classic min-max optimization problem: finding the adaptation parameters that minimize the maximum of the crossent...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Guang Chen, TonyX. Han
• Keywords: Adaptation Data, Adaptation Rates, Computer Vision, Conservative Model Adaptation, CVPR 2011