Saliency maps are used to predict the visual stimulus raised from a certain region in a scene. Most approaches to calculate the saliency in a scene can be divided into three consecutive steps: extraction of feature maps, calculation of activation maps, and the combination of activation maps. In the past two decades, several new saliency estimation approaches have emerged. However, most of these approaches are not freely available as source code, thus requiring researchers and application developers to reimplement them. Moreover, others are freely available but use different platforms for their implementation. As a result, employing, evaluating, and combining existing approaches is time consuming, costly, and even error-prone (e.g., when reimplementation is required). In this paper, we introduce the Saliency Sandbox, a framework for the fast implementation and prototyping of saliency maps, which employs a flexible architecture that allows designing new saliency maps by combining existing and new approaches such as Itti & Koch, GBVS, Boolean Maps and many more. The Saliency Sandbox comes with a large set of implemented feature extractors as well as some of the most popular activation approaches. The framework core is written in C++; nonetheless, interfaces for Matlab and Simulink allow for fast prototyping and integration of already existing implementations. Our source code is available at: www.ti.uni-tuebingen.de/perception.
