Statistics: concepts and applications
Statistics: concepts and applications
Effective retrieval of structured documents
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
MARCO: MAp Retrieval by COntent
IEEE Transactions on Pattern Analysis and Machine Intelligence
A flexible model for retrieval of SGML documents
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
G-Portal: a map-based digital library for distributed geospatial and georeferenced resources
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Information Retrieval from Documents: A Survey
Information Retrieval
Combining document representations for known-item search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Classification of source code archives
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Learning metadata from the evidence in an on-line citation matching scheme
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
A retrospective study of a hybrid document-context based retrieval model
Information Processing and Management: an International Journal
Geographically-aware information retrieval for collections of digitized historical maps
Proceedings of the 4th ACM workshop on Geographical information retrieval
Creating a searchable map library via data mining
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
| Hi-index | 0.00 |
Maps are an important source of information in archaeology and other sciences. Users want to search for historical maps to determine recorded history of the political geography of regions at different eras, to find out where exactly archaeological artifacts were discovered, etc. Currently, they have to use a generic search engine and add the term map along with other keywords to search for maps. This crude method will generate a significant number of false positives that the user will need to cull through to get the desired results. To reduce their manual effort, we propose an automatic map identification, indexing, and retrieval system that enables users to search and retrieve maps appearing in a large corpus of digital documents using simple keyword queries. We identify features that can help in distinguishing maps from other figures in digital documents and show how a Support-Vector-Machine-based classifier can be used to identify maps. We propose map-level-metadata e.g., captions, references to the maps in text, etc. and document-level metadata, e.g., title, abstract, citations, how recent the publication is, etc. and show how they can be automatically extracted and indexed. Our novel ranking algorithm weights different metadata fields differently and also uses the document-level metadata to help rank retrieved maps. Empirical evaluations show which features should be selected and which metadata fields should be weighted more. We also demonstrate improved retrieval results in comparison to adaptations of existing methods for map retrieval. Our map search engine has been deployed in an online map-search system that is part of the Blind-Review digital library system.