Incremental learning of object detectors using a visual shape alphabet
A. Opelt, A. Pinz and Andrew Zisserman
In: CVPR 2006, 17-22 June 2006, New York, USA.
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 sub-linearly 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 sharing of shape features not only reduces the number of features required per category, but also often im- proves recognition performance, as compared to individual detectors which are trained on a per-class basis.
|EPrint Type:||Conference or Workshop Item (Oral)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Mudigonda Pawan Kumar|
|Deposited On:||02 June 2006|