Probabilistic graphlet transfer for photo cropping

Luming Zhang, Mingli Song, Qi Zhao, Xiao Liu, Jiajun Bu, Chun Chen

Research output: Contribution to journalArticlepeer-review

120 Scopus citations

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.

Original languageEnglish (US)
Article number6327366
Pages (from-to)802-815
Number of pages14
JournalIEEE Transactions on Image Processing
Volume22
Issue number2
DOIs
StatePublished - Jan 21 2013

Keywords

  • Gibbs sampling
  • graphlet
  • probabilistic model
  • region adjacency graph

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