Figure 4

The compression ensemble approach is cognitively plausible and also performs well in algorithmic prediction of artwork authorship, date, style, genre, and medium. (A) and (B) exemplify two human ratings datasets, Multipic and Fractals (see text). (C) represents five art style period examples, Baroque, Realism, Impressionism, Expressionism, and Surrealism via central images in the ensemble for each style. (D) illustrates the difficulty of the artist detection task: while some artists produce very similar works, while also changing over their careers (Lawrence, Romney) others are more unique and hence recognizable (O’Keefe). Panels (E)-(I) illustrate mean testing accuracy given variable number of training items (light to dark blue) and number of transformations used (horizontal axis; the total number of features varies between tasks, as zero-variance and collinear ones are excluded). The dashed horizontal line is baseline chance accuracy for each task. Each dot stands for one added transformation feature, always starting with gif compression without transformation. The next 5 are given on each panel. Different transformations, ordered by variable importance, are informative in different tasks, e.g. color-related transformations in distinguishing paintings from drawings. Just compressing the image without transforming already provides an above-chance result in all cases, even on just a handful of training examples. Adding more transformations (dark blue dots) generally improves performance (when there is enough examples to avoid overfitting). That being said, around 15-20 well-chosen transformations are usually already enough to get close to maximal performance