Content based image retrieval for large medical image corpus
Date Issued
2015-06-22
Author(s)
Strezoski, Gjorgji
Stojanovski, Dario
Madjarov, Gjorgji
Abstract
In this paper we address the scalability issue when it comes
to Content based image retrieval in large image archives in the medical
domain. Throughout the text we focus on explaining how small changes
in image representation, using existing technologies leads to impressive
improvements when it comes to image indexing, search and retrieval duration. We used a combination of OpponentSIFT descriptors, Gaussian
Mixture Models, Fisher kernel and Product quantization that is neatly
packaged and ready for web integration. The CBIR feature of the system is demonstrated through a Python based web client with features
like region of interest selection and local image upload.
to Content based image retrieval in large image archives in the medical
domain. Throughout the text we focus on explaining how small changes
in image representation, using existing technologies leads to impressive
improvements when it comes to image indexing, search and retrieval duration. We used a combination of OpponentSIFT descriptors, Gaussian
Mixture Models, Fisher kernel and Product quantization that is neatly
packaged and ready for web integration. The CBIR feature of the system is demonstrated through a Python based web client with features
like region of interest selection and local image upload.
Subjects
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