Feature-preserving thumbnail generation based on graph cuts

A novel algorithm for thumbnail generation, which preserves characteristic features of a source image including blurs and textures, is proposed in this work. When a source image is subsampled to generate a thumbnail, important visible cues, such as blurs and noises, are lost. To overcome this drawback, we first create multiple thumbnail candidates that accentuate three classes of image features: focal blur, motion blur, and detail. Then, we obtain the final thumbnail by composing these candidates adaptively. Assuming that image features are spatially varying but locally static, we formulate the composition task as a labeling problem, and employ the graph-cut optimization technique to solve the problem. Simulation results demonstrate that the proposed algorithm provides feature-preserving thumbnails efficiently.