Utilizing Background Information for Generic Object Recognition

This thesis introduces background information to generic object recognition problem to increase the accuracy. Most of works do not divide images to foreground and background part, or only utilize foreground information. In this thesis, we tried to leverage background information to help object recognition. region of interest (ROI) detector is used to find the foreground object in images. Focusing on foreground object can reduce noisy features from unrelevant background region. Furthermore, the complement area of ROI can be considered as background context. Since objects in a category usually appear in specific context, we will show that adding background clue can improve the recognition accuracy in our experiment.nother challenge problem is how to use different signals together. We compared several methods of feature fusion for machine learning using SVM. Experiment result shows how well these methods can achieve and whether background information benefit them.