Abstract
| Original language | English |
|---|---|
| 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) |
|---|---|
| Country | Germany |
| City | Hamburg |
| Period | 28/09/15 → 2/10/15 |
| Internet address |
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Discriminative feature learning for efficient RGB-D object recognition. / Asif, Umar; Bennamoun, Mohammed; Sohel, Ferdous.
IEEE International Conference on Intelligent Robots and Systems. Vol. 2015 United States : IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 272-279.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - Discriminative feature learning for efficient RGB-D object recognition
AU - Asif, Umar
AU - Bennamoun, Mohammed
AU - Sohel, Ferdous
PY - 2015
Y1 - 2015
N2 - © 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.
AB - © 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.
U2 - 10.1109/IROS.2015.7353385
DO - 10.1109/IROS.2015.7353385
M3 - Conference paper
SN - 9781479999941
VL - 2015
SP - 272
EP - 279
BT - IEEE International Conference on Intelligent Robots and Systems
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
ER -