A comparative study of image retargeting

Abstract

CRSV MULTIOP SC SCL SM SNS WARP Figure 1: Example of retargeting the butterfly image shown in Figure 2 to half its size. In this study we evaluate 8 different image retargeting methods, asking users to compare their results and examine what qualities in retargeted images mattered to them. We also correlate the users' preferences with automatic image similarity measures. Our findings provide insights on the retargeting problem, and present a clear benchmark for future research in the field. AbstractThe numerous works on media retargeting call for a methodological approach for evaluating retargeting results. We present the first comprehensive perceptual study and analysis of image retargeting. First, we create a benchmark of images and conduct a large scale user study to compare a representative number of state-of-the-art retargeting methods. Second, we present analysis of the users' responses, where we find that humans in general agree on the evaluation of the results and show that some retargeting methods are consistently more favorable than others. Third, we examine whether computational image distance metrics can predict human retargeting perception. We show that current measures used in this context are not necessarily consistent with human rankings, and demonstrate that better results can be achieved using image features that were not previously considered for this task. We also reveal specific qualities in retargeted media that are more important for viewers. The importance of our work lies in promoting better measures to assess and guide retargeting algorithms in the future. The full benchmark we collected, including all images, retargeted results, and the collected user data, are available to the research community for further investigation at

Image Resizing in Saliency Histogram Domain

“…(2) Image warping uses a mesh M = (V, E, Q) composed of vertices V, edges E, and quads Q; V and E form grid lines to partition an image into Q. Wang et al proposed the OSS algorithm to map the input mesh M onto the deformed mesh M' with minimum distortion energy.…”

“…(1) Conventional methods using simple cropping and linear scaling are likely to degrade the region of interest (ROI). (2) In order to resize images/videos more effectively, it is desirable to take into account the visual content. (3) Modern resizing techniques are generally grouped into two categories: the discrete approach and the continuous approach.…”

Hybrid image retargeting using optimized seam carving and scaling

“…The RetargetMe dataset [28] of 80 images are used and 30 subjects of various age, gender, and education background are invited.…”

Seam warping: a new approach for image retargeting for small displays

“…5 are derived from reference (Liu and Gleicher 2005). All other images used in these figures are provided by the website "http://people.csail.milt.edu/mrub/ regargetme" which was built by Rubinstein and his partners (Rubinstein et al 2010). Figure 5 is a comparison of our seam warping method with classic seam carving method (Avidan and Shamir 2007).…”