Research Article
Feature Extraction with Ordered Mean Values for Content Based Image Classification
Table 10
Confusion matrix for Caltech dataset for feature extraction with ordered mean (KNN classifier).
| | a | b | c | d | e | f | g | h | i | j | k | l | m | n | o | p | q | r | s | t | ← classified as |
| | 69 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 2 | 0 | 0 | 0 | 6 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | ∣ a = Airplane | | 3 | 38 | 2 | 2 | 0 | 14 | 5 | 1 | 2 | 3 | 0 | 4 | 10 | 0 | 1 | 1 | 3 | 1 | 0 | 10 | ∣ b = Bonsai | | 0 | 1 | 18 | 0 | 0 | 11 | 2 | 1 | 1 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 1 | 0 | 0 | 7 | ∣ c = Panther | | 1 | 1 | 1 | 26 | 0 | 14 | 3 | 0 | 2 | 0 | 0 | 0 | 13 | 0 | 1 | 1 | 0 | 0 | 0 | 4 | ∣ d = Dalmatian | | 0 | 0 | 2 | 1 | 44 | 3 | 2 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | ∣ e = Dolphin | | 2 | 3 | 2 | 2 | 0 | 402 | 4 | 0 | 1 | 0 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 7 | 0 | 7 | ∣ f = Faces | | 1 | 1 | 1 | 1 | 0 | 16 | 31 | 1 | 0 | 0 | 0 | 0 | 4 | 2 | 1 | 2 | 2 | 1 | 0 | 3 | ∣ g = Flamingo | | 1 | 1 | 0 | 0 | 0 | 4 | 0 | 24 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | ∣ h = Deer | | 1 | 2 | 0 | 0 | 1 | 18 | 1 | 0 | 55 | 0 | 1 | 0 | 10 | 0 | 0 | 0 | 1 | 2 | 0 | 7 | ∣ i = Piano | | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 17 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | ∣ j = Skates | | 0 | 1 | 0 | 1 | 1 | 2 | 0 | 0 | 4 | 0 | 14 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | ∣ k = Metronome | | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 54 | 5 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | ∣ l = Minar | | 4 | 1 | 4 | 0 | 0 | 8 | 2 | 0 | 6 | 1 | 0 | 1 | 751 | 1 | 1 | 0 | 0 | 1 | 0 | 10 | ∣ m = Motorbike | | 1 | 2 | 1 | 1 | 0 | 3 | 2 | 0 | 1 | 0 | 0 | 1 | 1 | 16 | 1 | 0 | 0 | 1 | 0 | 4 | ∣ n = Panda | | 2 | 3 | 0 | 1 | 0 | 3 | 1 | 1 | 2 | 0 | 0 | 2 | 7 | 2 | 23 | 0 | 2 | 0 | 1 | 4 | ∣ o = Football | | 0 | 1 | 2 | 0 | 2 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 41 | 1 | 2 | 0 | 5 | ∣ p = Stopsign | | 1 | 1 | 1 | 0 | 0 | 8 | 0 | 2 | 1 | 0 | 1 | 0 | 4 | 1 | 0 | 0 | 58 | 1 | 0 | 6 | ∣ q = Sunflower | | 2 | 1 | 1 | 2 | 2 | 10 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 0 | 0 | 1 | 1 | 26 | 0 | 8 | ∣ r = Trees | | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 54 | 4 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | ∣ s = Monument | | 2 | 6 | 3 | 1 | 4 | 29 | 5 | 3 | 5 | 2 | 0 | 5 | 27 | 1 | 3 | 1 | 1 | 2 | 0 | 138 | ∣ t = Watches |
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