Incremental learning of object detectors using a visual shape alphabet

We address the problem of multiclass object detection. Our aims are to enable models for new categories to benefit from the detectors built previously for other categories, and for the complexity of the multiclass system to grow sublinearly with the number of categories. To this end we introduce a visual alphabet representation which can be learnt incrementally, and explicitly shares boundary fragments (contours) and spatial configurations (relation to centroid) across object categories. We develop a learning algorithm with the following novel contributions: (i) AdaBoost is adapted to learn jointly, based on shape features; (ii) a new learning schedule enables incremental additions of new categories; and (iii) the algorithm learns to detect objects (instead of categorizing images). Furthermore, we show that category similarities can be predicted from the alphabet. We obtain excellent experimental results on a variety of complex categories over several visual aspects. We show that the ...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Andreas Opelt, Axel Pinz, Andrew Zisserman
• Keywords: Complex Categories, Computer Vision, CVPR 2006, Multiclass Object Detection, Object Categories, Shape Features, Visual Alphabet Representation