Title: Learning to Rank Retargeted Images


Citation
Chen Y, Liu Y, Lai Y (2017). Learning to Rank Retargeted Images. Cardiff University. http://doi.org/10.17035/d.2017.0033306559



Access Rights: Data can be made freely available subject to attribution

Access Method: Click to email a request for this data to opendata@cardiff.ac.uk


Cardiff University Dataset Creators


Dataset Details

Publisher: Cardiff University

Date (year) of data becoming publicly available: 2017

Data format: .m

Software Required: MATLAB

Estimated total storage size of dataset: Less than 100 megabytes

DOI : 10.17035/d.2017.0033306559

DOI URL: http://doi.org/10.17035/d.2017.0033306559


Description

Image retargeting techniques that adjust images into different sizes have attracted much attention recently. Objective quality assessment (OQA) of image retargeting results is often desired to automatically select the best results. Existing OQA methods output an absolute score for each retargeted image and use these scores to compare different results. Observing that it is challenging even for human subjects to give consistent scores for retargeting results of different source images, in this paper we propose a learning-based OQA method that predicts the ranking of a set of retargeted images with the same source image. We show that this more manageable task helps achieve more consistent prediction to human preference and is sufficient for most application scenarios. To compute the ranking, we propose a simple yet efficient machine learning framework that uses a General Regression Neural Network (GRNN) to model a combination of seven elaborate OQA metrics. We then propose a simple scheme to transform the relative scores output from GRNN into a global ranking. We train our GRNN model using human preference data collected in the elaborate RetargetMe benchmark and evaluate our method based on the subjective study in RetargetMe. Moreover, we introduce a further subjective benchmark to evaluate the generalizability of different OQA methods. Experimental results demonstrate that our method outperforms eight representative OQA methods in ranking prediction and has better generalizability to different datasets. The data contains MATLAB code to support the research presented at IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, 21-26 July 2017.


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Last updated on 2019-22-07 at 08:28