Yonatan Tariku Tesfaye
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Yonatan Tariku Tesfaye has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
Eyasu Zemene
University of Central Florida
2 shared publicationsMarcello Pelillo
Ca’ Foscari University of Venice
2 shared publicationsAndrea Prati
University of Parma
1 shared publication- Andrea Prati
Department of Architecture and Engineering, University of Parma, Parma, Italy
1 shared publication Mubarak Shah
University of Central Florida
1 shared publication
Latest Publications
- Open Access
- 13 May 2019
- Published by Springer Nature in International Journal of Computer Vision
- Vol. 127 (9) , 1303-1320
Multi-target Tracking in Multiple Non-overlapping Cameras Using Fast-Constrained Dominant Sets
- 13 May 2019
- Published by Springer Nature in International Journal of Computer Vision
- Vol. 127 (9) , 1303-1320
Open AccessMulti-target Tracking in Multiple Non-overlapping Cameras Using Fast-Constrained Dominant Sets
- 1 December 2016
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2325-2330
Simultaneous clustering and outlier detection using dominant sets
We present a unified approach for simultaneous clustering and outlier detection in data. We utilize some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. Unlike most (all) of the previous techniques, in our framework the number of clusters arises intuitively and outliers are obliterated automatically. The resulting algorithm discovers both parameters from the data. Experiments on real and on large scale synthetic dataset demonstrate the effectiveness of our approach and the utility of carrying out both clustering and outlier detection in a concurrent manner.
- 1 December 2016
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2325-2330
Simultaneous clustering and outlier detection using dominant sets
We present a unified approach for simultaneous clustering and outlier detection in data. We utilize some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. Unlike most (all) of the previous techniques, in our framework the number of clusters arises intuitively and outliers are obliterated automatically. The resulting algorithm discovers both parameters from the data. Experiments on real and on large scale synthetic dataset demonstrate the effectiveness of our approach and the utility of carrying out both clustering and outlier detection in a concurrent manner.