Learning to rank retargeted images

Yang, Chen,, Yong-Jin, Liu, and Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 2017. Learning to rank retargeted images. Presented at: IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, 21-26 July 2017.

Preview

PDF - Accepted Post-Print Version Download (2MB) | Preview

Abstract

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 learningbased 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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Funders:	Royal Society
Date of First Compliant Deposit:	5 April 2017
Last Modified:	21 Oct 2022 07:21
URI:	https://orca.cardiff.ac.uk/id/eprint/99648

Actions (repository staff only)

Edit Item