Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Recognizing the intended message of line graphs
Diagrams'10 Proceedings of the 6th international conference on Diagrammatic representation and inference
The automated understanding of simple bar charts
Artificial Intelligence
Tag-based social image search with visual-text joint hypergraph learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Recognition and classification of figures in PDF documents
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Image and natural language processing for multimedia information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Information retrieval research has made significant progress in the retrieval of text documents and images. However, relatively little attention has been given to the retrieval of information graphics (non-pictorial images such as bar charts and line graphs) despite their proliferation in popular media such as newspapers and magazines. Our goal is to build a system for retrieving bar charts and line graphs that reasons about the content of the graphic itself in deciding its relevance to the user query. This paper presents the first steps toward such a system, with a focus on identifying the category of intended message of potentially relevant bar charts and line graphs. Our learned model achieves accuracy higher than 80\% on a corpus of collected user queries.