Picture retrieval by content description
Journal of Information Science
Content-Based Video Indexing and Retrieval
IEEE MultiMedia
Design and evaluation of algorithms for image retrieval by spatial similarity
ACM Transactions on Information Systems (TOIS)
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Querying multimedia data from multiple repositories by content: the Garlic project
Proceedings of the third IFIP WG2.6 working conference on Visual database systems 3 (VDB-3)
Visually Searching the Web for Content
IEEE MultiMedia
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic Queries with Pictures: The VIMSYS Model
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
ImageRover: A Content-Based Image Browser for the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Using Artificial Queries to Evaluate Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Integrated spatial and feature image query
Multimedia Systems
Effective invariant features for shape-based image retrieval: Research Articles
Journal of the American Society for Information Science and Technology
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Content-based retrieval (CBR) promises to greatly improve capabilities for searching for images based on semantic features and visual appearance. However, developing a framework for evaluating image retrieval effectiveness remains a significant challenge. Difficulties include determining how matching at different description levels affects relevance, designing meaningful benchmark queries of large image collections, and developing suitable quantitative metrics for measuring retrieval effectiveness. This article studies the problems of developing a framework and testbed for quantitative assessment of image retrieval effectiveness. In order to better harness the extensive research on CBR and improve capabilities of image retrieval systems, this article advocates the establishment of common image retrieval testbeds consisting of standardized image collections, benchmark queries, relevance assessments, and quantitative evaluation methods.