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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.
Additionally, we presented a FESB MLID dataset on which we conducted our research.
The authors would like to express their gratitude to everyone who contributed to the creation of the FESB MLID dataset, and especially to Marin Bugaric, Toni Jakovcevic and Maja Stula who obtained some of the images that are included in the dataset, and to the students from the University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture that helped to hand-label some of the images in the initial stages of the FESB MLID image database development.
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