New Zealand Journal of Agricultural Research abstracts
Study on a fish disease case reasoning system based on image
retrieval
Dongmei Lou
Ming Chen*
Jiechao Ye
College of Information Technology
Shanghai Fishery University
334 Jungong Road
Shanghai, 0, China
*Author for correspondence: mchen@shfu.edu.cn
Abstract To improve the accuracy of fish disease expert
system diagnosis, this paper develops a fish disease model which
combines case-based reasoning and content retrieval from images of sick
fish. In the visual inspection phase and microscope inspection phase of
fish disease diagnosis the system extracts the visual features of a
fish disease image which is uploaded by the user. It then uses
content-based image retrieval technology to find similar images in the
fish disease image database. This can be returned to the user as a
diagnosis reference and, at the same time it can be used as an index
for case retrieval. During case retrieval, depending on the similar
image’s semantic annotation index case database, the searching scope
can be reduced to improve the efficiency of case retrieval and the
accuracy of reasoning.
Keywords case-based reasoning; content-based fish disease
image retrieval; fish disease diagnosis expert system; image semantic
annotation
A07105; Online publication date 8 February 2008; Received and
accepted 10 August 2007
New Zealand Journal of Agricultural Research, 2007, Vol. 50:
887–893
0028–8233/07/5005–0887 © The Royal Society of New Zealand 2007
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