Recognizing Image Style

The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research. We describe an approach to predicting style of images, and perform a thorough evaluation of different image features for these tasks. We find that features learned in a multi-layer network generally perform best -- even when trained with object class (not style) labels. Our large-scale learning methods results in the best published performance on an existing dataset of aesthetic ratings and photographic style annotations. We present two novel datasets: 80K Flickr photographs annotated with 20 curated style labels, and 85K paintings annotated with 25 style/genre labels. Our approach shows excellent classification performance on both datasets. We use the learned classifiers to extend traditional tag-based image search to consider stylistic constraints, and demonstrate cross-dataset understanding of style

Drag Gesture Interpretation via a Fly-Through Ring Menu

The drag operation, performed via a mouse, touchscreen, or other input device, is a common gesture to move data or objects within a user interface. For identical drag paths, there may be different user intents for the drag operation. For example, a file can either be copied or moved along a given drag path. This disclosure describes efficient and intuitive techniques for disambiguating the intent of a drag operation without excessive user interaction. Upon commencement of a drag operation on an object, a floating, ring-shaped menu, referred to as a fly-through menu (FTM), appears automatically around the cursor or point of contact of the finger with the touchscreen. The circumference of the ring menu is divided into arcs labeled with menu selections for intents associated with the drag gesture. A user can execute the desired action by tracing a trajectory through the corresponding arc of the ring menu

Front- and Backmatter: Computational Aesthetics 2017

Computational Aesthetic

Light Waving: Estimating Light Positions From Photographs Alone

Computer Graphics Forum24

Stylized Vector Art from 3D Models with Region Support

We describe a rendering system that converts a 3D meshed model into the stylized 2D filled-region vector-art commonly found in clip-art libraries. To properly define filled regions, we analyze and combine accurate but jagged face-normal contours with smooth but inaccurate interpolated vertex normal contours, and construct a new smooth shadow contour that properly surrounds the actual jagged shadow contour. We decompose region definition into geometric and topological components, using machine precision for geometry processing and raster-precision to accelerate topological queries. We extend programmable stylization to simplify, smooth and stylize filled regions. The result renders 10K-face meshes into custom clip-art in seconds.Computer Graphics Forum27