  Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic
image dataset including original Environmental Microorganism (EM) images and
two sets of Ground Truth (GT) images. The GT image sets include a single-object
GT image set and a multi-object GT image set. The EMDS-5 dataset has 21 types
of EMs, each of which contains 20 original EM images, 20 single-object GT
images and 20 multi-object GT images. EMDS-5 can realize to evaluate image
preprocessing, image segmentation, feature extraction, image classification and
image retrieval functions. In order to prove the effectiveness of EMDS-5, for
each function, we select the most representative algorithms and price
indicators for testing and evaluation. The image preprocessing functions
contain two parts: image denoising and image edge detection. Image denoising
uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the
aspect of edge detection, six edge detection operators are used to detect the
edges of the images, and two evaluation indicators, peak-signal to noise ratio
and mean structural similarity, are used for evaluation. Image segmentation
includes single-object image segmentation and multi-object image segmentation.
Six methods are used for single-object image segmentation, while k-means and
U-net are used for multi-object segmentation.We extract nine features from the
images in EMDS-5 and use the Support Vector Machine classifier for testing. In
terms of image classification, we select the VGG16 feature to test different
classifiers. We test two types of retrieval approaches: texture feature
retrieval and deep learning feature retrieval. We select the last layer of
features of these two deep learning networks as feature vectors. We use mean
average precision as the evaluation index for retrieval.
