PDSearch: Using Pictures as Queries
Conference paper
Abstract
Search engines usually deliver a large amount results for each topic addressed by a few (mostly 2 or 3) keywords. Thus, it is a tough work to find those terms describing the wanted content in a manner such that the search delivers the intended results already on the first result pages. In the iterative process of obtaining the desired web pages, pictures with their tremendous context information may be a big help. This contribution presents an approach to include picture processing by humans as a means for context search selection and determination in a locally working search control.
Keywords
search engine context keyword metadata evaluation picture informationPreview
Unable to display preview. Download preview PDF.
References
- 1.November 2013 Web Server Survey 2013/11/01/november-2013-web-server-survey.html (2013), http://news.netcraft.com/archives/ (last retrieved on November 29, 2013)
- 2.Grimes, S.: Unstructured Data and the 80 Percent Rule (2008), http://breakthroughanalysis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/ (last retrieved on November 29, 2013)
- 3.Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998)Google Scholar
- 4.Website of Google Autocomplete, Web Search Help (2013), http://support.google.com/websearch/bin/answer.py?hl=en&answer=106230 (last retrieved on November 29, 2013)
- 5.Kubek, M., Witschel, H.F.: Searching the Web by Using the Knowledge in Local Text Documents. In: Proceedings of Mallorca Workshop 2010 Autonomous Systems. Shaker Verlag, Aachen (2010)Google Scholar
- 6.Website of DocAnalyser (2013), http://www.docanalyser.de (last retrieved on November 29, 2013)
- 7.Website of WebNavigator (2013), http://www.docanalyser.de/webnavigator (last retrieved on November 29, 2013)
- 8.Yee, K., Swearingen, K., Li, K., Hearst, M.: Faceted metadata for image search and browsing. In: CHI 2003 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 401–408 (2003)Google Scholar
- 9.Tushabe, F., Wilkinson, M.H.F.: Content-based Image Retrieval Using Combined 2D Attribute Pattern Spectra. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 554–561. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 10.Hawkins, J., Blakeslee, S.: On Intelligence: How a New Understanding of the Brain will Lead to the Creation of Truly Intelligent Machines. Times Books (2004)Google Scholar
- 11.Brisbane, A.: Speakers Give Sound Advice. Syracuse Post Standard, 18 (March 28, 1911)Google Scholar
- 12.Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)CrossRefGoogle Scholar
- 13.Heyer, G., Quasthoff, U., Wittig, T.: Text Mining: Wissensrohstoff Text: Konzepte, Algorithmen, Ergebnisse. W3L-Verlag, Dortmund (2006)Google Scholar
- 14.Kubek, M., Unger, H., Loauschasai, T.: A Quality- and Security-improved Web Search using Local Agents. Intl. Journal of Research in Engineering and Technology (IJRET) 1(6) (2012)Google Scholar
- 15.Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)MathSciNetCrossRefGoogle Scholar
Copyright information
© Springer International Publishing Switzerland 2014