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Which parts or objects are interesting in a content? In this paper we first propose three computational models to automatically predict interestingness rankings of areas/objects inside a 2D picture. We based our modeling on previous experimental findings to ensure reliability of the prediction when compared to the human assessement of interestingness. Our two first models are based on low level features, extracted from image regions, which have been stated as useful in the human interest process. A baseline model is built by estimating a linear regression from a small dataset of 49 images. The second model estimates a rewarding term based on additional experimental observations. By adding image semantics, we then construct a last model, which more generally benefits from a better understanding of the content. It also integrates notions such that unusualness or human beings' presence that have proven to play key roles in the interestingness process. Finally, targeting VR applications, we extend our models to immersive content, both images and videos, and propose an innovative application to guide the viewer in his/her navigation based on intuitive visual or audio cues.
