Multi-query Content Based Medical Image Retrieval
Date Issued
2013
Author(s)
Stojanova, Elena
Loshkovska, Suzana
Abstract
This paper focuses on applying multi-query single-group
methods to improve the content based image retrieval
performance. The Multi-query-Max and Multi-query-Avg
methods were applied using different numbers of query
examples, namely three, five, and ten. The dataset contained
medical images. The results obtained from the multi-query
methods are compared to the single-query approach. The
multi-query outperformed the single-query approach in all
cases, meaning three, five, and ten queries based retrieval.
Additionally, the Multi-query-Max method gives the best
results on the bases of MAP (Mean Average Precision) value,
when for the feature extraction purposes the Edge Histogram
Descriptor (EHD) is used.
methods to improve the content based image retrieval
performance. The Multi-query-Max and Multi-query-Avg
methods were applied using different numbers of query
examples, namely three, five, and ten. The dataset contained
medical images. The results obtained from the multi-query
methods are compared to the single-query approach. The
multi-query outperformed the single-query approach in all
cases, meaning three, five, and ten queries based retrieval.
Additionally, the Multi-query-Max method gives the best
results on the bases of MAP (Mean Average Precision) value,
when for the feature extraction purposes the Edge Histogram
Descriptor (EHD) is used.
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