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Towards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study

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Academic year: 2021

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Figure

Figure 1: Left: A single tree induced from a training set of random subwindows, using node tests with single pixel thresholding, for the ET-FL scheme
Figure 3: Results averaged over all 80 datasets for ET-FL variant: First column: Average of error rates for all datasets with subwindow size intervals (1st row), image representation (2nd), number of random tests (3rd), number of trees (4th), minimum node
Figure 2: Summary of the best error rate obtained for each dataset without optimizations, gathered from Supplementary Tables II to XII
Figure 5: Several datasets for which larger subwindow sizes yield lowest error rates.

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