Name
Abstract | ||
|---|---|---|
Data retrieval serves a critical role in the development of multimedia applications. However, due to the exponential growth of multimedia data, high-speed and efficient indexing is becoming more and more difficult than ever. In this paper, we propose a novel approach to speed up the retrieval process by adopting a distributed computing paradigm through the Apache Spark framework. Utilizing search trees in a Big Data ecosystem leads to fast and cost-effective media database retrievals by caching indexing structures into memory and aggregating ranked results with flexibilities for users to specify the importance of search cues. We conducted computational experiments on large-scaled vector files for remote sensing image database and synthesized pollen image database to demonstrate the effectiveness and scalability of our system with reasonably high accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISM.2017.38 | 2017 IEEE International Symposium on Multimedia (ISM) |
Keywords | Field | DocType |
In-Memory Computing,Big Data,Distributed Indexing,Image Database Retrieval | Vector graphics,Spark (mathematics),Information retrieval,Ranking,Pattern recognition,Computer science,Data retrieval,Search engine indexing,Artificial intelligence,Big data,Scalability,Speedup | Conference |
ISBN | Citations | PageRank |
978-1-5386-2938-3 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinmiao Ma | 1 | 0 | 0.34 |
| Danlu Liu | 2 | 1 | 3.06 |
| Grant J. Scott | 3 | 214 | 22.19 |
| Jeffrey K. Uhlmann | 4 | 2435 | 263.94 |
| Chi-Ren Shyu | 5 | 656 | 67.58 |