Pthreads programming
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust content-based image searches for copyright protection
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
An attention-driven model for grouping similar images with image retrieval applications
EURASIP Journal on Applied Signal Processing
Localizing volumetric motion for action recognition in realistic videos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Fast near duplicate detection for personal image collections
MM '09 Proceedings of the 17th ACM international conference on Multimedia
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Feature tracking and matching in video using programmable graphics hardware
Machine Vision and Applications
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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With explosive growth of multimedia data on internet, the effective information retrieval from a large scale of multimedia data becomes more and more important. To retrieve these multimedia data automatically, some features in them must be extracted. Hence, image feature extraction algorithms have been a fundamental component of multimedia retrieval. Among these algorithms, Scale Invariant Feature Transform (SIFT) has been proven to be one of the most robust image feature extraction algorithm. However, SIFT algorithm is not only data intensive but also computation intensive. It takes about four seconds to process an image or a video frame on a general-purpose CPU, which is far from real-time processing requirement. Therefore, accelerating SIFT algorithm is urgently needed. As multi-core CPU becomes more and more popular in recent years, it is natural to employ computing power of multi-core CPU to accelerate SIFT. How to parallelize SIFT to take full use of multi-core capabilities becomes one of the core issues. This paper analyzes available parallelism in SIFT and implements various parallel SIFT algorithms to evaluate which is the most suitable for multicore system. The final result shows that our parallel SIFT achieves a speedup of 10.46X on 16-core machine.