Abstract
We are interested here in retrieving images containing a specific person in image database. Due to large variations in illumination conditions, hairstyles, facial expressions, etc. and the factors like occlusion, sunglasses, profile, etc., robust face matching has been a challenging problem. On the other hand, the speed of search is also a considerable issue, especially for the dataset with millions of face images. Inspired by face tracks in video retrieval which take advantages from the abundance of frames to get multiple exemplars, we present an approach named multi balanced trees for face retrieval from image dataset in this paper. Face images in the dataset are efficiently organized by the trees produced for persons. Multi sampling on the facial components employs the rich local information, which can help to differentiate different persons. Given a query face, a sorted face set with similarities is obtained by inserting the query into a tree. It is easy and fast to get the search results in respect that it avoids calculating the distances between query and elements in the cluster. In addition, a rectification strategy is given in the query process to rectify the error occurred in the generation of trees, resulting in a significant improvement of retrieval quality. Experimental results show the better face grouping ability in comparison with traditional methods. The speed of searching is improved as well.
| Original language | English |
|---|---|
| Title of host publication | 2011 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2011 |
| Pages | 484-489 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2011 |
| Event | 2011 2nd IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2011 - Kuala Lumpur Duration: 2011 Nov 16 → 2011 Nov 18 |
Other
| Other | 2011 2nd IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2011 |
|---|---|
| City | Kuala Lumpur |
| Period | 11/11/16 → 11/11/18 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Multi balanced trees for face retrieval from image database. / Hao, Pengyi; Kamata, Seiichiro.
2011 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2011. 2011. p. 484-489 6144094.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multi balanced trees for face retrieval from image database
AU - Hao, Pengyi
AU - Kamata, Seiichiro
PY - 2011
Y1 - 2011
N2 - We are interested here in retrieving images containing a specific person in image database. Due to large variations in illumination conditions, hairstyles, facial expressions, etc. and the factors like occlusion, sunglasses, profile, etc., robust face matching has been a challenging problem. On the other hand, the speed of search is also a considerable issue, especially for the dataset with millions of face images. Inspired by face tracks in video retrieval which take advantages from the abundance of frames to get multiple exemplars, we present an approach named multi balanced trees for face retrieval from image dataset in this paper. Face images in the dataset are efficiently organized by the trees produced for persons. Multi sampling on the facial components employs the rich local information, which can help to differentiate different persons. Given a query face, a sorted face set with similarities is obtained by inserting the query into a tree. It is easy and fast to get the search results in respect that it avoids calculating the distances between query and elements in the cluster. In addition, a rectification strategy is given in the query process to rectify the error occurred in the generation of trees, resulting in a significant improvement of retrieval quality. Experimental results show the better face grouping ability in comparison with traditional methods. The speed of searching is improved as well.
AB - We are interested here in retrieving images containing a specific person in image database. Due to large variations in illumination conditions, hairstyles, facial expressions, etc. and the factors like occlusion, sunglasses, profile, etc., robust face matching has been a challenging problem. On the other hand, the speed of search is also a considerable issue, especially for the dataset with millions of face images. Inspired by face tracks in video retrieval which take advantages from the abundance of frames to get multiple exemplars, we present an approach named multi balanced trees for face retrieval from image dataset in this paper. Face images in the dataset are efficiently organized by the trees produced for persons. Multi sampling on the facial components employs the rich local information, which can help to differentiate different persons. Given a query face, a sorted face set with similarities is obtained by inserting the query into a tree. It is easy and fast to get the search results in respect that it avoids calculating the distances between query and elements in the cluster. In addition, a rectification strategy is given in the query process to rectify the error occurred in the generation of trees, resulting in a significant improvement of retrieval quality. Experimental results show the better face grouping ability in comparison with traditional methods. The speed of searching is improved as well.
UR - http://www.scopus.com/inward/record.url?scp=84857483915&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84857483915&partnerID=8YFLogxK
U2 - 10.1109/ICSIPA.2011.6144094
DO - 10.1109/ICSIPA.2011.6144094
M3 - Conference contribution
AN - SCOPUS:84857483915
SN - 9781457702419
SP - 484
EP - 489
BT - 2011 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2011
ER -