ScribbleBoost: Adding Classification to Edge-Aware Interpolation of Local Image and Video Adjustments

Simple item page
dc.contributor.authorLi, Y.en_US
dc.contributor.authorAdelson, E.en_US
dc.contributor.authorAgarwala, A.en_US
dc.date.accessioned2015-02-21T17:06:23Z
dc.date.available2015-02-21T17:06:23Z
dc.date.issued2008en_US
dc.description.abstractOne of the most common tasks in image and video editing is the local adjustment of various properties (e.g., saturation or brightness) of regions within an image or video. Edge-aware interpolation of user-drawn scribbles offers a less effort-intensive approach to this problem than traditional region selection and matting. However, the technique suffers a number of limitations, such as reduced performance in the presence of texture contrast, and the inability to handle fragmented appearances. We significantly improve the performance of edge-aware interpolation for this problem by adding a boosting-based classification step that learns to discriminate between the appearance of scribbled pixels. We show that this novel data term in combination with an existing edge-aware optimization technique achieves substantially better results for the local image and video adjustment problem than edge-aware interpolation techniques without classification, or related methods such as matting techniques or graph cut segmentation.en_US
dc.description.number4en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume27en_US
dc.identifier.doi10.1111/j.1467-8659.2008.01264.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages1255-1264en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2008.01264.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleScribbleBoost: Adding Classification to Edge-Aware Interpolation of Local Image and Video Adjustmentsen_US
Files
Collections
27-Issue 4
EGSR08: 19th Eurographics Symposium on Rendering