Journal of Systems and Software
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Digital Image Processing
Generalized data retrieval for pyramid-based periodic broadcasting of videos
Future Generation Computer Systems - Special issue: Semantic grid and knowledge grid: the next-generation web
Contains and inside relationships within combinatorial pyramids
Pattern Recognition
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Content Based Image Retrieval Using Adaptive Inverse Pyramid Representation
Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009
A new pyramid-based color image representation for visual localization
Image and Vision Computing
Steerable pyramid-based face hallucination
Pattern Recognition
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Computers in Biology and Medicine
Discriminative compact pyramids for object and scene recognition
Pattern Recognition
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In hospitals and medical institutes, a large number of mammograms are produced in ever increasing quantities and used for diagnostics and therapy. The need for effective methods to manage and retrieve those image resources has been actively pursued in the medical community. This paper proposes a hierarchical correlation calculation approach to content-based mammogram retrieval. In this approach, images are represented as a Gaussian pyramid with several reduced-resolution levels. A global search is first conducted to identify the optimal matching position, where the correlation between the query image and the target images in the database is maximal. Local search is performed in the region comprising the four child pixels at a higher resolution level to locate the position with maximal correlation at greater resolution. Finally, this position with the maximal correlation found at the finest resolution level is used as the image similarity measure for retrieving images. Experimental results have shown that this approach achieves 59% in precision and 54% in recall when the threshold of correlation is 0.5.