Shared-memory parallelization for content-based image retrieval

In this paper we show how modern shared-memory parallelization techniques can gain nearly linear speedup in content-based image retrieval. Using OpenMP, few changes are applied to the source code to enable the exploitation of the capabilities of current multi-core/multiprocessor systems. These techniques allow the use of computationally expensive methods in interactive retrieval scenarios which has not been possible so far. In addition, these ideas were applied to a clustering algorithm where substantial performance improvements could be observed as well.

[1]  Hermann Ney,et al.  Features for Image Retrieval: A Quantitative Comparison , 2004, DAGM-Symposium.

[2]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Mingjing Li,et al.  MyPhotos: a system for home photo management and processing , 2002, MULTIMEDIA '02.

[4]  Lai-Man Po,et al.  Web-based Beowulf-Class parallel computing on image database indexing and retrieval system , 2001, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489).

[5]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[6]  Frederick Jelinek,et al.  Statistical methods for speech recognition , 1997 .

[7]  Thomas Martin Deserno,et al.  The CLEF 2005 Cross-Language Image Retrieval Track , 2005, CLEF.

[8]  Mark Sanderson,et al.  The CLEF Cross Language Image Retrieval Track (ImageCLEF) 2004 , 2004, CLEF.

[9]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[10]  Hermann Ney,et al.  Discriminative training for object recognition using image patches , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  O. Kao,et al.  On parallel image retrieval with dynamically extracted features , 2008, Parallel Comput..

[12]  Alberto Del Bimbo,et al.  Merging Results for Distributed Content Based Image Retrieval , 2004, Multimedia Tools and Applications.

[13]  Hermann Ney,et al.  Local context in non-linear deformation models for handwritten character recognition , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..