Weakly supervised discriminative localization and classification: a joint learning process

Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g. in terms of window size and location) which may be suboptimal for classification. In this report we propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Minh Hoai Nguyen, Lorenzo Torresani, Fernando de la Torre, Carsten Rother
• Keywords: Computer Vision, ICCV 2009, Pattern Recognition