Towards edge-caching for image recognition

2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017

Volumn , Issue , 2017, Pages 593-598

  Drolia, Utsav ^a   Guo, Katherine ^b   Tan, Jiaqi ^a   Gandhi, Rajeev ^a   Narasimhan, Priya ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b LUCENT TECHNOLOGIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIGITAL STORAGE; IMAGE RECOGNITION;

COMPUTATION TIME; REAL-TIME APPLICATION; RECOGNITION ALGORITHM; SEAMLESS EXPERIENCE; STATE-OF-THE-ART SYSTEM; STORAGE CAPABILITY; SURROUNDING ENVIRONMENT; USER PERCEPTIONS;

UBIQUITOUS COMPUTING;

EID: 85020014818     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PERCOMW.2017.7917629     Document Type: Conference Paper

References (26)

1. Bing Vision. hups://en.wikipedia.org/wikilBing⋯Vision.
2. Google Glass. https://en.wikipedia.orglwiki/Google-Glass.
3. Google Goggls. https://en. wikipedia.org/wiki/Google-Goggles.
4. Microsoft Hololens. huttps://www.microsofLcom/microsoft-hololens/en-us.
5. Nokia Point & Find. https://en.wikipedia.orglwikilNokia]oinC%26-Find.
6. Wikitude. http://www.wikitude.coml.
7. P. Bahl, R. Y. Han, L. E. Li, and M. Satyanarayanan. Advancing the state of mobile cloud computing. In ACM Workshop on Mobile Cloud Computing and Services, 2012.
8. H. Bay, T. Tuytelaars, and L. Van Gool. Surf: Speeded up robust features. In Computer Vision-ECCV 2006, pages 404-417. Springer, 2006.
9. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC'12, pages 13-16, New York, NY, USA, 2012. ACM.
10. L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and zipf-like distributions: Evidence and implications. In INFOCOM'99. Eighteenth Annual loint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, volume 1, pages 126-134. IEEE, 1999.
11. Y. R. Chandrasekhar, D. M. Chen, S. S. Tsai, N.-M. Cheung, H. Chen, G. Takacs, Y. Reznik, R. Vedantham, R. Grzeszczuk, J. Bach, et al. The stanford mobile visual search data set. In Proceedings of the second annual ACM conference on Multimedia systems, pages 117-122. ACM, 2011.
12. T. Y.-H. Chen, L. Ravindranath, S. Deng, P. Bah!, and H. Balakrishnan. Glimpse: Continuous, real-time object recognition on mobile devices. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pages 155-168. ACM, 2015.
13. M. Datar, N. Immorlica, P. Indyk, and Y. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the Twentieth Annual Symposium on Computational Geometry, SCG'04, pages 253-262, New York, NY, USA, 2004. ACM.
14. K. Ha, P. Pillai, G. Lewis, S. Simanta, S. Clinch, N. Davies, and M. Satyanarayanan. The impact of mobile multimedia applications on data center consolidation. In Cloud Engineering (IC2E), 2013 IEEE International Conference on, pages 166-176. IEEE, 2013.
15. S. Han, H. Shen, M. Philipose, S. Agarwal, A. Wolman, and A. Krishnamurthy. Mcdnn: An approximation-based execution framework for deep stream processing under resource constraints. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, pages 123-136. ACM, 2016.
16. P. Jain, J. Manweiler, and R. Roy Choudhury. Overlay: P ractical mobile augmented reality. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys'15, pages 331-344, New York, NY, USA, 2015. ACM.
17. W. Kelly. Computer vision and the future of mobile devices. http://www.techrepublic.com/article/computer-vision-and-the-future-of-mobile-devices/, August 2014.
18. N. D. Lane, S. Bhattacharya, P. Georgiev, C. Forlivesi, L. Jiao, L. Qendro, and F. Kawsar. Deepx: A software accelerator for low-power deep learning inference on mobile devices. In 2016 15th ACMIIEEE International Conference on information Processing in Sensor Networks (IPSN), pages 1-12. IEEE, 2016.
19. Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436-444, 2015.
20. D. G. Lowe. Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, volume 2, pages 1150-1157. Ieee, 1999.
21. B. M. Maggs and R. K. Sitaraman. Algorithmic nuggets in content delivery. ACM SIGCOMM Computer Communication Review, 45(3):52-66, 2015.
22. E. Rublee, Y. Rabaud, K. Konolige, and G. Bradski. Orb: An efficient alternative to sift or surf. In 2011 International conference on computer vision, pages 2564-2571. IEEE, 2011.
23. M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies. The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4):14-23, Oct. 2009.
24. C. Silpa-Anan and R. Hartley. Optimised kd-trees for fast image descriptor matching. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE, 2008.
25. R. Szeliski. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
26. X. Wang, M. Yang, T. Cour, S. Zhu, K. Yu, and T. X. Han. Contextual weighting for vocabulary tree based image retrieval. In 2011 International Conference on Computer Vision, pages 209-216. IEEE, 2011.