Abstract
Feature extraction is one of the essential parts of multimedia indexing in similarity search and content-based retrieval methods. Most applications that employ these methods also implement their client side interface using web technologies. The world wide web has become a well-established platform for distributed software and virtually all personal computers, tablets, and smartphones are equipped with a web browser. In the past, most applications employed a strict client-server approach, where the client part (running in the browser) handles only the user interface and the server side handles data storage and business logic. However, the client-side technologies leaped forward with the new HTML5 standard and the web browser has become capable of handling much more complex tasks. In this paper, we propose a model where the multimedia indexing is handled at the client side, which reduces necessary computational power of the server to run a web application that manages large multimedia database. We have implemented an in-browser image feature extractor and compared its performance with a server implementation.
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Chien, A., Calder, B., Elbert, S., Bhatia, K.: Entropia: architecture and performance of an enterprise desktop grid system. Journal of Parallel Distributed Computing 65, 597–610 (2003)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR) 40(2), 5 (2008)
Duda, J., Dłubacz, W.: Distributed evolutionary computing system based on web browsers with JavaScript. In: Manninen, P., Öster, P. (eds.) PARA 2012. LNCS, vol. 7782, pp. 183–191. Springer, Heidelberg (2013)
Garrett, J.J., et al.: Ajax: A new approach to web applications (2005)
Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A k-means clustering algorithm. Applied Statistics, 100–108 (1979)
Hickson, I., Hyatt, D.: Html5. W3C Working Draft, May 2011
Jeon, W., Brutch, T., Gibbs, S.: Webcl for hardware-accelerated web applications. In: TIZEN Developer Conference May, pp. 7–9 (2012)
Krulis, M., Falt, Z., Zavoral, F.: Exploiting HTML5 technologies for distributed parasitic web storge. In: Proceedings of the Dateso 2014 Annual International Workshop on DAtabases, TExts, Specifications and Objects, Roudnice nad Labem, Czech Republic, 16 April 2014, pp. 71–80 (2014). http://ceur-ws.org/Vol-1139/poster10.pdf
Kruliš, M., Lokoč, J., Skopal, T.: Efficient extraction of feature signatures using multi-GPU architecture. In: Li, S., El Saddik, A., Wang, M., Mei, T., Sebe, N., Yan, S., Hong, R., Gurrin, C. (eds.) MMM 2013, Part II. LNCS, vol. 7733, pp. 446–456. Springer, Heidelberg (2013)
McLaren, K.: XIII–The Development of the CIE 1976 (L* a* b*) Uniform Colour Space and Colour-difference Formula. Journal of the Society of Dyers and Colourists 92(9), 338–341 (1976)
Merelo-Guervós, J.J., Castillo, P.A., Laredo, J.L.J., Mora Garcia, A., Prieto, A.: Asynchronous distributed genetic algorithms with javascript and json. In: IEEE Congress on Evolutionary Computation, CEC 2008, (IEEE World Congress on Computational Intelligence), pp. 1372–1379. IEEE (2008)
Reginald, C., Putra, G., Belloum, A., Koulouzis, S., Bubak, M., de Laat, C.: Distributed Computing on an Ensemble of Browsers (2013)
Univ. of Berkeley: SETI@Home (2006). http://setiathome.ssl.berkeley.edu/
W3C: Web Workers. http://www.w3.org/TR/workers/
Wang, M., Ni, B., Hua, X.S., Chua, T.S.: Assistive tagging: A survey of multimedia tagging with human-computer joint exploration. ACM Comput. Surv. 44(4), 25:1–25:24 (2012). http://doi.acm.org/10.1145/2333112.2333120
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Kruliš, M. (2015). Is There a Free Lunch for Image Feature Extraction in Web Applications. In: Amato, G., Connor, R., Falchi, F., Gennaro, C. (eds) Similarity Search and Applications. SISAP 2015. Lecture Notes in Computer Science(), vol 9371. Springer, Cham. https://doi.org/10.1007/978-3-319-25087-8_31
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DOI: https://doi.org/10.1007/978-3-319-25087-8_31
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