This work explores the creation of ambiguous images, i.e., images that may induce multistable perception, by evolutionary means. Ambiguous images are created using a general purpose approach, composed of an expression-based evolutionary engine and a set of object detectors, which are trained in advance using Machine Learning techniques. Images are evolved using Genetic Programming and object detectors are used to classify them. The information gathered during classification is used to assign fitness. In a first stage, the system is used to evolve images that resemble a single object. In a second stage, the discovery of ambiguous images is promoted by combining pairs of object detectors. The analysis of the results highlights the ability of the system to evolve ambiguous images and the differences between computational and human ambiguous images.
|Title of host publication||Proceedings of the twenty-fourth International Joint Conference on Artificial Intelligence (IJCAI 2015|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 2015|
|Event||24th International Joint Conference on Artificial Intelligence - Buenos Aires, Argentina|
Duration: 25 Jul 2015 → 31 Jul 2015
|Conference||24th International Joint Conference on Artificial Intelligence|
|Abbreviated title||IJCAI 2015|
|Period||25/07/15 → 31/07/15|
Bibliographical noteFunding: Fundação para a Ciêencia e Tecnologia (FCT), Portugal (SFRH/BD/90968/2012); and European Commission 7FP (project ConCreTe, Future and
Emerging Technologies (FET) (611733).