Reduced Reference Quality Assessment for Image Retargeting by Earth Mover’s Distance

A reduced reference quality assessment algorithm for image retargeting by earth mover’s distance is proposed in this paper. In the reference image, all the feature points are extracted using scale invariant feature transform. Let the histograms of image patch around each feature point be local information, and the histograms of saliency feature as global information. Those feature information is extracted at the sender side and transmitted to the receiver side. After that, the same feature information extraction process is performed for the retargeted image at the receiver side. Finally, all feature information of the reference and retargeted images is used collectively to compute the quality of the retargeted image. An overall quality score is calculated from the local and global similarity measure using earth mover’s distance between reference and retargeted images. The key step in our algorithm is to provide an earth mover’s distance metric in a manner that indicates how the local and global information in the reference image is preserved in corresponding retargeted image. Experimental results show that the proposed algorithm can improve the image quality scores on four common criteria in the retargeted image quality assessment community.

Spatial and Texture Analysis of Root System Architecture with Earth Mover's Distance (STARSEED)

Root system architectures are complex, multidimensional, and challenging to characterize effectively for agronomic and ecological discovery. We propose a new method, Spatial and Texture Analysis of Root System architEcture with Earth mover's Distance (STARSEED), for comparing root architectures that incorporate spatial information through a novel application of the Earth Mover's Distance (EMD).We illustrate that the approach captures the response of sesame root systems for different genotypes and soil moisture levels. STARSEED provides quantitative and visual insights into changes that occur in root architectures across experimental treatments.STARSEED can be easily generalized to other plants and provides insight into root system architecture development and response to varying growth conditions not captured by existing root architecture metrics and models. The code and data for our experiments are publicly available: https://github.com/GatorSense/STARSEED.

Spatial and Texture Analysis of Root System Architecture with Earth Mover's Distance

Root system architectures are complex, multidimensional, and challenging to characterize effectively for agronomic and ecological discovery. We propose a new method, Spatial and Texture Analysis of Root System architEcture with Earth mover's Distance (STARSEED), for comparing root architectures that incorporate spatial information through a novel application of the Earth Mover's Distance (EMD). We illustrate that the approach captures the response of sesame root systems for different genotypes and soil moisture levels. STARSEED provides quantitative and visual insights into changes that occur in root architectures across experimental treatments. STARSEED can be easily generalized to other plants and provides insight into root system architecture development and response to varying growth conditions not captured by existing root architecture metrics and models. The code and data for our experiments are publicly available https://github.com/GatorSense/STARSEED.