DocumentCode :
3328156
Title :
Network of evolutionary binary classifiers for classification and retrieval in macroinvertebrate databases
Author :
Kiranyaz, Serkan ; Gabbouj, Moncef ; Pulkkinen, Jenni ; Ince, Turker ; Meissner, Kristian
Author_Institution :
Tampere Univ. of Technol., Tampere, Finland
fYear :
2010
fDate :
26-29 Sept. 2010
Firstpage :
2257
Lastpage :
2260
Abstract :
In this paper, we focus on advanced classification and data retrieval schemes that are instrumental when processing large taxonomical image datasets. With large number of classes, classification and an efficient retrieval of a particular benthic macroinvertebrate image within a dataset will surely pose a severe problem. To address this, we propose a novel network of evolutionary binary classifiers, which is scalable, dynamically adaptable and highly accurate for the classification and retrieval of large biological species-image datasets. The classification and retrieval results for the macroinvertebrate test data attain taxonomic accuracy that equals and even surpasses that of an average expert. Our findings are encouraging for aquatic biomonitoring where cost intensity of sample analysis currently poses a bottleneck for routine biomonitoring.
Keywords :
pattern classification; data retrieval scheme; evolutionary binary classifier; macroinvertebrate databases; taxonomical image dataset; Accuracy; Artificial neural networks; Databases; Feature extraction; Humans; Support vector machine classification; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
ISSN :
1522-4880
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2010.5651161
Filename :
5651161
Link To Document :
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