caa a22 4500 445379588 CHVBK 20180317143013.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s10032-010-0136-2 doi (NATIONALLICENCE)springer-10.1007/s10032-010-0136-2 Harvesting maps on the web [Elektronische Daten] [Aman Goel, Matthew Michelson, Craig Knoblock] Maps are one of the most valuable documents for gathering geospatial information about a region. Yet, finding a collection of diverse, high-quality maps is a significant challenge because there is a dearth of content-specific metadata available to identify them from among other images on the Web. For this reason, it is desirous to analyze the content of each image. The problem is further complicated by the variations between different types of maps, such as street maps and contour maps, and also by the fact that many high-quality maps are embedded within other documents such as PDF reports. In this paper, we present an automatic method to find high-quality maps for a given geographic region. Not only does our method find documents that are maps, but also those that are embedded within other documents. We have developed a Content-Based Image Retrieval (CBIR) approach that uses a new set of features for classification in order to capture the defining characteristics of a map. This approach is able to identify all types of maps irrespective of their subject, scale, and color in a highly scalable and accurate way. Our classifier achieves an F1-measure of 74%, which is an 18% improvement over the previous work in the area. Springer-Verlag, 2010 Map identification nationallicence K-nearest neighbor classifier nationallicence Water-filling features nationallicence Content-Based Image Retrieval (CBIR) nationallicence PDF documents nationallicence Goel Aman Department of Computer Science and Information Sciences Institute, University of Southern California, 90292, Marina del Rey, CA, USA aut Michelson Matthew Fetch Technologies, 841 Apollo St. Suite 400, 90245, El Segundo, CA, USA aut Knoblock Craig Department of Computer Science and Information Sciences Institute, University of Southern California, 90292, Marina del Rey, CA, USA aut International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/4(2011-12-01), 349-372 1433-2833 14:4<349 2011 14 10032 https://doi.org/10.1007/s10032-010-0136-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-010-0136-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Goel Aman Department of Computer Science and Information Sciences Institute, University of Southern California, 90292, Marina del Rey, CA, USA aut NATIONALLICENCE 700 1- Michelson Matthew Fetch Technologies, 841 Apollo St. Suite 400, 90245, El Segundo, CA, USA aut NATIONALLICENCE 700 1- Knoblock Craig Department of Computer Science and Information Sciences Institute, University of Southern California, 90292, Marina del Rey, CA, USA aut NATIONALLICENCE 773 0- International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/4(2011-12-01), 349-372 1433-2833 14:4<349 2011 14 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer