Share Email Print

Proceedings Paper

Semi-automatic photo clustering with distance metric learning
Author(s): Dinghuang Ji; Meng Wang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Photo clustering has been widely explored in many applications such as album management. But automatic clustering can hardly achieve satisfying performance due to the large variety of photos' content. This paper proposes a semi-automatic photo clustering scheme that attempts to improve clustering performance with users' interactions. Users can adjust the results of automatic clustering, and a set of constraints among photos are generated accordingly. A distance metric is then learned with these constraints and we can re-implement clustering with this metric. We conduct experiments on different photo albums, and experimental results have demonstrated that our approach is able to improve automatic photo clustering results, and it is better than pure manual adjustment approach by exploring distance metric learning.

Paper Details

Date Published: 14 July 2010
PDF: 8 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 774409 (14 July 2010); doi: 10.1117/12.863499
Show Author Affiliations
Dinghuang Ji, Institute of Computing Technology (China)
Meng Wang, Microsoft Research Asia (China)

Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

© SPIE. Terms of Use
Back to Top