Object recognition using segmentation for feature detection (OeAGM)
Michael Fussenegger, Andreas Opelt, Axel Pinz and Peter Auer
In: ÖAGM 2004, 17-18 Jun 2004, Hagenberg, Austria.
A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a set of typical regions, and use a new segmentation method – “Similarity-Measure Segmentation” – to split the images into regions of interest. This approach may also deliver segments, which are split into several disconnected parts, which turns out to be a powerful description of local similarities. Several textual features are calculated for each region, which are used to learn object categories with Boosting. We demonstrate the flexibility and power of our method by excellent results on various datasets. In comparison, our recognition results are significantly higher than results published in related work.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Peter Auer|
|Deposited On:||29 December 2004|