Emotions in Abstract Art: Does Texture Matter?

The classification of images based on the emotions they evoke is a recent approach in multimedia. With the abundance of digitized images from museum archives and the ever-growing digital production of user-generated images, there is a greater need for intelligent image retrieval algorithms. Categorization of images according to their emotional impact offers a useful addition to the state of the art in image search. In this work, we apply computer vision techniques on abstract paintings to automatically predict emotional valence based on texture. We also propose a method to derive a small set of features (Perlin parameters) from an image to represent its overall texture. Finally, we investigate the saliency distribution in these images, and show that computational models of bottom-up attention can be used to predict emotional valence in a parsimonious manner.

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