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Supplemental Information

Figure 1: Simulation Results

Performance of the bag-of-features classifier using 100 feature clusters. (Left) Classification accuracy given training on the ten largest categories in the Caltech 101 sample set. Colored lines show accuracy for specific categories, while dashed black lines show overall accuracy for each level of training complexity. (Right) Accuracy by a classifier trained on 102 categories during a test in which n stimuli must be classified correctly for a trial to be ‘correct.’ Performance for the classifier’s ten best (black) and worst (white) categories was gauged. Solid lines indicate cases in which classification was done perfectly, while dashed lines indicate cases where correct responses required at least one guess.

DOI: 10.7287/peerj.preprints.688v1/supp-1

Additional Information

Competing Interests

The authors declare they have no competing interests.

Author Contributions

Greg Jensen analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Drew Altschul analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.


Financial support was provided by NIH grant 5-R01-MH081153-06 awarded to V. Ferrera and H. Terrace. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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