Name
Title | ||
|---|---|---|
An efficient and scalable learning algorithm for Near-Earth objects detection in astronomy big image data |
Abstract | ||
|---|---|---|
In this paper, we investigate the efficiency and scalability of Gaussian mixture model based learning algorithm for the detection of Near-Earth objects in large scale astronomy image data. We propose an effective scheme to reduce the computational complexity of current learning algorithm, this is achieved by adopting the perceptual image hashing method. Our proposed scheme is validated on raw astronomy image data. The experiment results illustrate that both efficiency and scalability are improved significantly in astronomical scenario and other scenario. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SMC.2014.6973999 | SMC |
Keywords | DocType | ISSN |
scalable learning algorithm,learning (artificial intelligence),astronomical image processing,large scale astronomy image data,Gaussian mixture model based learning algorithm,computational complexity,perceptual image hashing method,near-Earth object detection,Gaussian processes,object detection | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||