 We present a large crowdsourcing (CS) study to examine how specific image attributes probabilistically affect the selection and sequencing of images from personal photo collections. 13 image attributes are explored, including 7 human-centric properties. We first propose a novel dataset shaping technique based on Mixed Integer Linear Programming (MILP), to identify a subset of photos in which the attributes of interest are (a) uniformly distributed and (b) minimally correlated. This allows the synthesis of compact, balanced representative datasets, allowing the efficient modeling of crowd preferences by learning through CS (i) the selection likelihood of an image and (ii) its relative position in a sequence, given its attributes. Then, we present an ILP-based slideshow creation framework to select and arrange (a subset of) appealing/interesting images from a personal photo library. A user-study confirms that our method considerably outperforms random photo selection and sequencing, while generating slideshows similar in quality to those created by humans.  This page contains the supplementary material for the paper (at the bottom of the page). It includes:
The provided code for (3) and (4) requires the images from the Gallagher dataset to demonstrate the results. For obvious copyright reasons we cannot release these images.You may download them from this link: http://chenlab.ece.cornell.edu/people/Andy/GallagherDataset.html The supplementary material in available in the following zip file. Please click on the down arrow on the bottom right to download it.
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