Many people are interested in taking astonishing photos and sharing with
others. Emerging hightech hardware and software facilitate ubiquitousness and
functionality of digital photography. Because composition matters in
photography, researchers have leveraged some common composition techniques to
assess the aesthetic quality of photos computationally. However, composition
techniques developed by professionals are far more diverse than well-documented
techniques can cover. We leverage the vast underexplored innovations in
photography for computational composition assistance. We propose a
comprehensive framework, named CAPTAIN (Composition Assistance for Photo
Taking), containing integrated deep-learned semantic detectors, sub-genre
categorization, artistic pose clustering, personalized aesthetics-based image
retrieval, and style set matching. The framework is backed by a large dataset
crawled from a photo-sharing Website with mostly photography enthusiasts and
professionals. The work proposes a sequence of steps that have not been
explored in the past by researchers. The work addresses personal preferences
for composition through presenting a ranked-list of photographs to the user
based on user-specified weights in the similarity measure. The matching
algorithm recognizes the best shot among a sequence of shots with respect to
the user's preferred style set. We have conducted a number of experiments on
the newly proposed components and reported findings. A user study demonstrates
that the work is useful to those taking photos.
CAPTAIN: Comprehensive Composition Assistance for Photo Taking
CAPTAIN: Comprehensive Composition Assistance for Photo Taking
