Beauty is in the eye of the beholder:Demographically oriented analysis of aesthetics in photographs

Aesthetics is a subjective concept that is likely to be perceived differently among people of different ages, genders, and cultural backgrounds. While techniques that directly compute this concept in images has seen increasing attention by the multimedia and machine-learning community, there are very few attempts at encoding the influences from the photographer’s viewpoint. This work demonstrates how the aesthetic quality of photos can be better learned by accounting for the demographic background of a photographer. A new AVA-PD (Photographer Demographic) dataset is created to supplement the AVA dataset by providing photographers’ age, gender and location attributes. Two deep convolutional neural network (CNN) architectures are proposed to utilize demographic information for aesthetic prediction of photos; both are shown to yield better prediction capabilities compared to most existing approaches. By leveraging on AVA-PD meta-data, we also present some additional machine-learnable tasks such as identifying the photographer and predicting photography styles from a person’s gallery of photos

The design and empirical evaluations of 3D positioning techniques for pressure-based touch control on mobile devices

The previous three degrees of freedom (DOF) 3D touch translations require more than one finger (usually two hands) to be performed, which limits their usability on mobile devices that need one hand to be held in most occasions. Given that the pressure-sensitive touch screen will become ubiquitous in the near future, we presented a pressure-based 3DOF 3D positioning technique that only uses one finger in operating. Our technique collects the normal force of the touch pressure and uses it to represent the depth value in 3D translating. Then we conducted several groups of tightly controlled user studies to conclude (1) how different strategies of pressure recognition will affect 3D translating and (2) how is the performance of the pressure-based manipulation compared to the previous two-fingered technique. Finally, we discussed some guidelines to help developers in the design of the pressure-sensing technique in 3D manipulations