In Visual Computing applications, several problems arise that involve a 
large number of parameters and have a strong sub jective component. An 
example is the photographer problem, which tries to optimize the position, 
orientation and field of view to capture a tridimensional scene. Design 
Galleries are commonly used to support the user in such multi-parameter 
optimization. However, repeatedly choosing parameters for several instances 
of a problem turns usual Design Galleries labourious and does not use 
previous user interactions to help him. This work presents Intelligent 
Galleries, a learning approach for sub jective problems such as the camera 
placement. The interaction of the user with a design gallery teaches a 
statistical learning machine. The trained machine can then imitate the 
user, either by classifying parameters or by automatically searching the 
best one. The learning process relies on a Support Vector Machines for 
classifying views from a collection of descriptors. We perform experiments 
with the automatic camera placement, which demonstrate that the proposed 
technique is efficient and handles scenes with occlusion and high depth 
complexities. This work also includes user validations of the intelligent 
gallery interface.