FESB

AcronymDefinition
FESBFakultet Elektrotehnike, Strojarstva i Brodogradnje (Croatian: Faculty of Electrical Engineering, Mechanical Engineering and Shipbuilding; University of Split)
FESBFreie Evangelische Schulen Berlin (German: Berlin Free Evangelical Schools; Berlin, Germany)
FESBFiorano Enterprise Service Bus (middleware infrastructure platform)
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References in periodicals archive ?
From the 200 train images from the FESB MLID dataset we calculated color probability histograms that tell us, for each class and for each color channel of particular color space, how likely it is that a certain value of the color channel will appear for a certain class.
Let us suppose that we want to classify one image pixel P(x,y,z) from one test image from the FESB MLID dataset in the RGB color space, so x, y and z are red, green and blue values of that pixel.
We have applied cogent confabulation color-based classification algorithm to 200 test images from the FESB MLID test dataset, and for each image we obtained 9 segmented and classified images, one for each color space that we used.
The results presented in this table were obtained for the 200 test images from the FESB MLID dataset.
These results are calculated for 200 test images from the FESB MLID dataset.
For contextual information we have calculated the average probabilities of each class that show how likely it is that a particular class will appear in the top, middle, or bottom part of the image on 200 train images from the FESB MLID dataset.
In this section of the paper we give details about the FESB MLID dataset that we used in this paper, and we compare the proposed expert system with two well-known classifiers--Normal Bayes and MLP.
Mediterranean Landscape Image Dataset (FESB MLID, http://wildfire.fesb.hr/) is a set of 400 natural Mediterranean landscape images along with their ground truth (GT) segmentations.
Out of the 400 images currently in the FESB MLID dataset, the first 200 are used as train images and the second 200 as test images in this paper.
We found these classifiers to be problematic in our case because of the computer memory and speed complications that occurred when we tried to classify 200 test images from the FESB MLID dataset with them.
Finally, we trained the MLP and Normal Bayes classifiers on 200 train images from the FESB MLID dataset.
11 show average classification results for the MLP classifier, Normal Bayes classifier, and the proposed CCB expert system for 200 test images from the FESB MLID dataset.