Learning a hierarchical compositional representation of multiple object classes
Aleš Leonardis · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · 2009
Visual categorization, recognition, and detection of objects has been an area of active research in the vision community for decades. Ultimately, the goal is to recognize and detect a large number of object classes in images within an acceptable time frame. This problem entangles three highly interconnected issues: the internal object representation which should expand sublinearly with the number of classes, means to learn the representation from a set of images, and an effective inference algorithm that matches the object representation against the representation produced from the scene.