SEMI-LOCAL FEATURES FOR THE CLASSIFICATION OF SEGMENTED OBJECTS

Robert Sorschag

2012

Abstract

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.

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Paper Citation


in Harvard Style

Sorschag R. (2012). SEMI-LOCAL FEATURES FOR THE CLASSIFICATION OF SEGMENTED OBJECTS . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 170-175. DOI: 10.5220/0003712301700175


in Bibtex Style

@conference{icpram12,
author={Robert Sorschag},
title={SEMI-LOCAL FEATURES FOR THE CLASSIFICATION OF SEGMENTED OBJECTS},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={170-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003712301700175},
isbn={978-989-8425-98-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - SEMI-LOCAL FEATURES FOR THE CLASSIFICATION OF SEGMENTED OBJECTS
SN - 978-989-8425-98-0
AU - Sorschag R.
PY - 2012
SP - 170
EP - 175
DO - 10.5220/0003712301700175