Object recognition/detection has always been a challenging topic for both computer scientists and psychologists. We study the categorical object recognition/detection problem from views of both computer vision and human vision, with the focus on feature representation. We propose representing general object categories using multiple types of complementary features, and using AdaBoost to select the most discriminative features. We also present a computational model which describes human eye move ...