Figure 4: Classification results. To quantify the difference in low-level feature statistics across categories (see Figure~\ref{fig:chevrons2}), we used a standard Support Vector Machine (SVM) classifier to measure how each representation affected the classifier's reliability for identifying the image category. For each individual image, we constructed a vector of features as either (FO) the histogram of first-order statistics as the histogram of edges' orientations, (CM) the chevron map subset of the second-order statistics, (i.e., the two-dimensional histogram of relative orientation and azimuth; see Figure 2 ), or (SO) the full, four-dimensional histogram of second-order statistics (i.e., all parameters of the edge co-occurrences). We gathered these vectors for each different class of images and report here the results of the SVM classifier using an F1 score (50\% represents chance level). While it was expected that differences would be clear between non-animal natural images versus laboratory (man-made) images, results are still quite high for classifying animal images versus non-animal natural images, and are in the range reported by~\citet{Serre07} (F1 score of 80\% for human observers and 82\% for their model), even using the CM features alone. We further extend this results to the psychophysical results of Serre et al. (2007) in Figure 5. Go back to manuscript page.
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