Probabilistic Graphlet Transfer for Photo Cropping
Citations Over TimeTop 1% of 2012 papers
Abstract
As one of the most basic photo manipulation processes, photo cropping is widely used in the printing, graphic design, and photography industries. In this paper, we introduce graphlets (i.e., small connected subgraphs) to represent a photo's aesthetic features, and propose a probabilistic model to transfer aesthetic features from the training photo onto the cropped photo. In particular, by segmenting each photo into a set of regions, we construct a region adjacency graph (RAG) to represent the global aesthetic feature of each photo. Graphlets are then extracted from the RAGs, and these graphlets capture the local aesthetic features of the photos. Finally, we cast photo cropping as a candidate-searching procedure on the basis of a probabilistic model, and infer the parameters of the cropped photos using Gibbs sampling. The proposed method is fully automatic. Subjective evaluations have shown that it is preferred over a number of existing approaches.
Related Papers
- → A more efficient Gibbs sampler estimation using steady-state simulation: applications to public health studies(2013)4 cited
- → The Gibbs Sampler(2010)3 cited
- Creating a Photo Laboratory Combined with Traditional Photography and Digital Photography(2010)
- Adjacency-Based Heuristic for Facilities Design(2005)
- → The Conception of ‘Realism as the Specificity of Photographic Medium’ in Korean Photography(2023)