Abstract
© 2015 IEEE. This paper presents an efficient approach to recognize objects captured with an RGB-D sensor. The proposed approach uses a Bag-of-Words (BOW) model to learn feature representations from raw RGB-D point clouds in a weakly supervised manner. To this end, we introduce a novel method based on randomized clustering trees to learn visual vocabularies which are fast to compute and more discriminative compared to the vocabularies generated by classical methods such as k-means. We show that, when combined with standard spatial pooling strategies, our proposed approach yields a powerful feature representation for RGB-D object recognition. Our extensive experimental evaluation on two challenging RGB-D object datasets and live video streams from Kinect shows that our learned features result in superior object recognition accuracies compared with the state-of-the-art methods.
Original language | English |
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Title of host publication | IEEE International Conference on Intelligent Robots and Systems |
Place of Publication | United States |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 272-279 |
Volume | 2015 |
ISBN (Print) | 9781479999941 |
DOIs | |
Publication status | Published - 2015 |
Event | 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Congress Center Hamburg Am Dammtor / Marseiller Straße, Hamburg, Germany Duration: 28 Sep 2015 → 2 Oct 2015 http://www.iros2015.org |
Conference
Conference | 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
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Country | Germany |
City | Hamburg |
Period | 28/09/15 → 2/10/15 |
Internet address |