Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2012.2223226
Title: Probabilistic graphlet transfer for photo cropping
Authors: Zhang, L.
Song, M.
Zhao, Q. 
Liu, X.
Bu, J.
Chen, C.
Keywords: Gibbs sampling
graphlet
probabilistic model
region adjacency graph
Issue Date: 2013
Source: Zhang, L., Song, M., Zhao, Q., Liu, X., Bu, J., Chen, C. (2013). Probabilistic graphlet transfer for photo cropping. IEEE Transactions on Image Processing 22 (2) : 802-815. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2012.2223226
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. © 1992-2012 IEEE.
Source Title: IEEE Transactions on Image Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/57121
ISSN: 10577149
DOI: 10.1109/TIP.2012.2223226
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