A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The design and analysis of spatial data structures
The design and analysis of spatial data structures
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Communications of the ACM
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Multimedia Information Retrieval: Content-Based Information Retrieval from Large Text and Audio Databases
Information Retrieval
The Perils of Interpreting Recall and Precision Values
Proceedings of the GI/GMD-Workshop on Information Retrieval
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Identifying Maps on the World Wide Web
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Image similarity: from syntax to weak semantics
Multimedia Tools and Applications
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Content-based image retrieval in astronomy needs methods that can deal with an image content made of noisy and diffuse structures. This motivates investigations on how information should be summarized and indexed for this specific kind of images. The method we present first summarizes the image information content by partitioning the image in regions with same texture. We call this process texture summarization. Second, indexing features are generated by examining the distribution of parameters describing image regions. Indexing features can be associated with global or local image characteristics. Both kinds of indexing features are evaluated on the retrieval system of the Zurich archive of solar radio spectrograms. The evaluation shows that generating local indexing features using self-organizing maps yields the best effectiveness of all tested methods.