Abstract
Multimedia retrieval task is faced with increasingly large datasets and variously changing preferences of users in every query. We realize that the high dimensional representation of physical data which previously challenges search algorithms now brings chances to cope with dynamic contexts. In this paper, we introduce a method of building a large-scale video frame retrieval environment with a fast search algorithm that handles user's dynamic contexts of querying by imagination and controlling response time. The search algorithm quickly finds an initial candidate, which has highest-match possibility, and then iteratively traverses along feature indexes to find other neighbor candidates until the input time bound is elapsed. The experimental studies based on the video frame retrieval system show the feasibility and effectiveness of our proposed search algorithm that can return results in a fraction of a second with a high success rate and small deviation to the expected ones. Moreover, its potential is clear that it can scale to large dataset while preserving its search performance.
| Original language | English |
|---|---|
| Title of host publication | Frontiers in Artificial Intelligence and Applications |
| Publisher | IOS Press |
| Pages | 324-342 |
| Number of pages | 19 |
| Volume | 272 |
| ISBN (Print) | 9781614994718 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 24th International Conference on Information Modelling and Knowledge Bases, EJC 2014 - Kiel, Germany Duration: 2014 Jun 3 → 2014 Jun 6 |
Publication series
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Volume | 272 |
| ISSN (Print) | 09226389 |
Other
| Other | 24th International Conference on Information Modelling and Knowledge Bases, EJC 2014 |
|---|---|
| Country | Germany |
| City | Kiel |
| Period | 14/6/3 → 14/6/6 |
Keywords
- Adaptive response time
- Inverted index
- Large-scale multimedia retrieval
- Local search
- Video navigation
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
An adaptive search path traverse for large-scale video frame retrieval. / Nguyen, Diep Thi Ngoc; Kiyoki, Yasushi.
Frontiers in Artificial Intelligence and Applications. Vol. 272 IOS Press, 2014. p. 324-342 (Frontiers in Artificial Intelligence and Applications; Vol. 272).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An adaptive search path traverse for large-scale video frame retrieval
AU - Nguyen, Diep Thi Ngoc
AU - Kiyoki, Yasushi
PY - 2014
Y1 - 2014
N2 - Multimedia retrieval task is faced with increasingly large datasets and variously changing preferences of users in every query. We realize that the high dimensional representation of physical data which previously challenges search algorithms now brings chances to cope with dynamic contexts. In this paper, we introduce a method of building a large-scale video frame retrieval environment with a fast search algorithm that handles user's dynamic contexts of querying by imagination and controlling response time. The search algorithm quickly finds an initial candidate, which has highest-match possibility, and then iteratively traverses along feature indexes to find other neighbor candidates until the input time bound is elapsed. The experimental studies based on the video frame retrieval system show the feasibility and effectiveness of our proposed search algorithm that can return results in a fraction of a second with a high success rate and small deviation to the expected ones. Moreover, its potential is clear that it can scale to large dataset while preserving its search performance.
AB - Multimedia retrieval task is faced with increasingly large datasets and variously changing preferences of users in every query. We realize that the high dimensional representation of physical data which previously challenges search algorithms now brings chances to cope with dynamic contexts. In this paper, we introduce a method of building a large-scale video frame retrieval environment with a fast search algorithm that handles user's dynamic contexts of querying by imagination and controlling response time. The search algorithm quickly finds an initial candidate, which has highest-match possibility, and then iteratively traverses along feature indexes to find other neighbor candidates until the input time bound is elapsed. The experimental studies based on the video frame retrieval system show the feasibility and effectiveness of our proposed search algorithm that can return results in a fraction of a second with a high success rate and small deviation to the expected ones. Moreover, its potential is clear that it can scale to large dataset while preserving its search performance.
KW - Adaptive response time
KW - Inverted index
KW - Large-scale multimedia retrieval
KW - Local search
KW - Video navigation
UR - http://www.scopus.com/inward/record.url?scp=84922548729&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84922548729&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-472-5-324
DO - 10.3233/978-1-61499-472-5-324
M3 - Conference contribution
SN - 9781614994718
VL - 272
T3 - Frontiers in Artificial Intelligence and Applications
SP - 324
EP - 342
BT - Frontiers in Artificial Intelligence and Applications
PB - IOS Press
ER -