Cathleen D. Cerosaletti

Automatic aesthetic value assessment in photographic images

The automatic assessment of aesthetic values in consumer photographic images is an important issue for content management, organizing and retrieving images, and building digital image albums. This paper explores automatic aesthetic estimation in two different tasks: (1) to estimate fine-granularity aesthetic scores ranging from 0 to 100, a novel regression method, namely Diff-RankBoost, is proposed based on RankBoost and support vector techniques; and (2) to predict coarse-granularity aesthetic categories (e.g., visually “very pleasing” or “not pleasing”), multi-category classifiers are developed. A set of visual features describing various characteristics related to image quality and aesthetic values are used to generate multidimensional feature spaces for aesthetic estimation. Experiments over a consumer photographic image collection with user ground-truth indicate that the proposed algorithms provide promising results for automatic image aesthetic assessment.

Measuring the perceived aesthetic quality of photographic images

There have been few studies that empirically assess the perception of aesthetics in photography. This study addressed a few image attributes that were hypothesized to be important to the perception of aesthetics. Thirty consumer photographers evaluated 450 images in a repeated measures factorial experiment and provided artistic quality ratings on a 0 to 100-point scale. The image set balanced main subject size, images with people and without people, and the type of perspective cue. Three first-party scenes supplied by each observer were evaluated by all observers in the study. Study results indicate that aesthetic preferences were mediated by all three of the study variables as a significant threeway interaction. Principal component and cluster analyses provided further insight into other features that impact aesthetic preference. Images in this study formed a continuum of quality ranging from low aesthetic and low technical quality to high aesthetic and high technical quality.

Creating a comfortable stereoscopic viewing experience: effects of viewing distance and field of view on fusional range

In stereoscopic display systems, there is always a balance between creating a “wow factor,” using large horizontal disparities, and providing a comfortable viewing environment for the user. In this paper, we explore the range of horizontal disparities, which can be fused by a human observer, as a function of the viewing distance and the field of view of the display. Two studies were conducted to evaluate the performance of human observers in a stereoscopic viewing environment. The viewing distance was varied in the first study using a CRT with shutter glasses. The second study employed a large field-of-view display with infinity focus, and the simulated field of view was varied. The recorded responses included fusion/no fusion, fusion time, and degree of convergence. The results show that viewing distance has a small impact on the angular fusional range. In contrast, the field of view has a much stronger impact on the angular fusional range. A link between the degree of convergence and the fusional range is demonstrated. This link suggests that the capability of the human observer to perform eye vergence movements to achieve stereoscopic fusion may be the limiting factor in fusing large horizontal disparities presented in stereoscopic displays.

Investigating two features of aesthetic perception in consumer photographic images: clutter and center

A key aspect of image effectiveness is how well the image visually communicates the main subject. In consumer images, two important features that impact viewer appreciation of the main subject are the amount of clutter and the main subject placement within the image. Two subjective experiments were conducted to assess the relationship between aesthetic and technical quality and perception of clutter and image center. For each experiment, 30 participants evaluated the same 70 images, on 0 to 100-point scales for aesthetic and technical quality. For the clutter experiment, participants also evaluated the images, on 0 to 100-point scales for amount of clutter and main subject emphasis. For the center experiment, participants pointed directly onto the image to mark the center of interest. Results indicate that aesthetic quality, technical quality, amount of clutter, and main subject emphasis are strongly correlated. Based on 95% confidence ellipses and mean-shift clustering, expert main subject maps are consistent with observer identification of main subject location. Further, the distribution of the observer identification of the center of interest is related to the object class (e.g., person, scenery). Additional features related to image composition can be used to explain clusters formed by patterns of mean ratings.

Semantics Meets UX: Mediating Intelligent Indexing of Consumers' Multimedia Collections for Multifaceted Visualization and Media Creation

Unorganized media collections hinder consumers from fully experiencing and enjoying their visual media. User interfaces can mediate the results of automated indexing by presenting data and interactions that leverage the strengths of individual and combined algorithm results, supporting multifaceted browsing, and enabling user correction in a way that is not disruptive to the userspsila activities. We describe the semantic system demonstration framework (SSDF), a flexible and extensible framework for combining multiple semantic indexing algorithms for consumer photo and video clip collections into one integrated system. We also describe key features of Koi, an SSDF desktop client application with a user interface designed to mediate and leverage the intelligent indexing incorporated in the SSDF server. Together, Koi and SSDF empower users to experience their personal multimedia in novel and sophisticated ways.