In this paper, we present a recommendation system for the automatic design of magazine covers. Our users are non-designer designers: individuals or small and medium businesses who want to design without hiring a professional designer while still wanting to create aesthetically compelling designs. Because a design should have a purpose, we suggest a number of semantic features to the user, e.g., "clean and clear," "dynamic and active," or "formal," to describe the color mood for the purpose of his/her design. Based on these high level features and a number of low level features, such as the complexity of the visual balance in a photo, our system selects the best photos from the user's album for his/her design. Our system then generates several alternative designs that can be rated by the user. Consequently, our system generates future designs based on the user's style. In this fashion, our system personalizes the designs of a user based on his/her preferences.

Managing large document databases has become an important task. Being able to automatically compare document layouts and classify and search documents with respect to their visual appearance proves to be desirable in many applications. We propose a new algorithm that approximates a metric function between documents based on their visual similarity. The comparison is based only on the visual appearance of the document without taking into consideration its text content. We measure the similarity of single page documents with respect to distance functions between three document components: background, text, and saliency. Each document component is represented as a Gaussian mixture distribution; and distances between the components of different documents are calculated as an approximation of the Hellinger distance between corresponding distributions. Since the Hellinger distance obeys the triangle inequality, it proves to be favorable in the task of nearest neighbor search in a document database. Thus, the computation required to find similar documents in a document database can be significantly reduced.

We present a new paradigm for automated document composition based on a generative, unified probabilistic document model (PDM) that models document composition. The model formally incorporates key design variables such as content pagination, relative arrangement possibilities for page elements and possible page edits. These design choices are modeled jointly as coupled random variables (a Bayesian Network) with uncertainty modeled by their probability distributions. The overall joint probability distribution for the network assigns higher probability to good design choices. Given this model, we show that the general document layout problem can be reduced to probabilistic inference over the Bayesian network. We show that the inference task may be accomplished efficiently, scaling linearly with the content in the best case. We provide a useful specialization of the general model and use it to illustrate the advantages of soft probabilistic encodings over hard one-way constraints in specifying design aesthetics.

Photobook is a popular form adopted by consumers to organize photos with text into pages for memory preserving. In this paper, a photobook creation and social sharing application developed on facebook platform is presented with the focus on the following perspectives: 1) imaging and layout technologies for photobook creation; 2) cloud based architecture design; 3) social photobook sharing. The application is live on facebook with proven data on user growth.

We demonstrate a system for automatic image selection for photobook creation, along with an intuitive user interface for fine tuning of the selection results. A versatile image collection representation is introduced, which allows for automatic scalable selection in order to target a specific image count for a predetermined size photobook. The images are selected based on their relevance, while preserving a good coverage of the event (time plus people) in order to maintain the storytelling potential of the selection. The selected images are laid out and presented to the user through an Adobe Flex user interface, which allows them to select images and swap them by semantically related ones, in an intuitive manner. The final result is output to a PDF file.

With the increasing amount of digital media being stored, shared and consumed on the Internet, more imaging applications are deployed online. However, the usability and use model of those applications are often less studied. In this paper, we present Snapfish Lab, an open online imaging community web site, to enable both imaging technologists and enthusiasts participate actively in exploring the usability and use model of the exploratory imaging applications. The main topic addressed in this paper is the challenges and solutions of building a production quality web site that supports open and high performance imaging application hosting, rich user feedback collection and satisfactory end user experience. The key value offered by Snapfish Lab is to bridge imaging research community and end users so that user’s demand becomes part of innovation cycle to drive web based imaging research and development.