Image features are usually extracted globally from whole images or locally from regions-of-interest. We propose different approaches to extract semi-local features from segmented objects in the context of object detection. The focus lies on the transformation of arbitrarily shaped object segments to image regions that are suitable for the extraction of features like SIFT, Gabor wavelets, and MPEG-7 color features. In this region transformation step, decisions arise about the used region boundary size and about modifications of the object and its background. Amongst others, we compare uniformly colored, blurred and randomly sampled backgrounds versus simple bounding boxes without object-background modifications. An extensive evaluation on the Pascal VOC 2010 segmentation dataset indicates that semi-local features are suitable for this task and that a significant difference exists between different feature extraction methods.
R. Sorschag: "Semi-Local Features for the Classification of Segmented Objects"; Talk: 1st International Conference on Pattern Recognition Applications and Methods, Vilamoura, Algarve, Portugal; 02-06-2012 - 02-08-2012; in: "Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods", SciTePress, 1 (2012), ISBN: 978-989-8425-98-0; 170 - 175.
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