Abstract

This paper presents a powerful framework for generic object recognition. Boosting is used as an underlying learning technique. For the first time a combination of various weak classifiers of different types of descriptors is used, which slightly increases the classification result but dramatically improves the stability of a classifier. Besides applying well known techniques to extract salient regions we also present a new segmentation method-“Similarity-Measure-Segmentation”. This approach delivers segments, which can consist of several disconnected parts. This turns out to be a mighty description of local similarity. With regard to the task of object categorization, Similarity-Measure-Segmentation performs equal or better than current state-of-the-art segmentation techniques. In contrast to previous solutions we aim at handling of complex objects appearing in highly cluttered images. Therefore we have set up a database containing images with the required complexity. On these images we obtain very good classification results of up to 87 % ROC-equal error rate. Focusing the performance on common databases for object recognition our approach outperforms all comparable solutions.

Citations