Content-based vessel image retrieval

Satabdi Mukherjee; Samuel Cohen; Izidor Gertner

doi:10.1117/12.2234847

12 May 2016 Content-based vessel image retrieval

Satabdi Mukherjee, Samuel Cohen, Izidor Gertner

Proceedings Volume 9844, Automatic Target Recognition XXVI; 984412 (2016) https://doi.org/10.1117/12.2234847
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

This paper describes an approach to vessel classification from satellite images using content based image retrieval methodology. Content-based image retrieval is an important problem in both medical imaging and surveillance applications. In many cases the archived reference database is not fully structured, thus making content-based image retrieval a challenging problem. In addition, in surveillance applications, the query image may be affected by weather or/and geometric distortions. Our approach of content-based vessel image retrieval consists of two phases. First, we create a structured reference database, then for each new query image of a vessel we find the closest cluster of images in the structured reference database, thus identifying and classifying the vessel. Then we update the closest cluster with new query image.

Citation Download Citation

Satabdi Mukherjee, Samuel Cohen, and Izidor Gertner "Content-based vessel image retrieval", Proc. SPIE 9844, Automatic Target Recognition XXVI, 984412 (12 May 2016); https://doi.org/10.1117/12.2234847

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KEYWORDS

Databases

Image processing

Fused deposition modeling

Image retrieval

Data modeling

Binary data

Content based image retrieval

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