Saliency-guided Adaptive Seeding for Supervoxel Segmentation
This is an incremental improvement for 3D computer vision tasks like object segmentation.
The paper tackled the problem of generating supervoxels in 3D space by proposing a saliency-guided method that improves segmentation quality, resulting in better boundary recall and under-segmentation error on benchmarks.
We propose a new saliency-guided method for generating supervoxels in 3D space. Rather than using an evenly distributed spatial seeding procedure, our method uses visual saliency to guide the process of supervoxel generation. This results in densely distributed, small, and precise supervoxels in salient regions which often contain objects, and larger supervoxels in less salient regions that often correspond to background. Our approach largely improves the quality of the resulting supervoxel segmentation in terms of boundary recall and under-segmentation error on publicly available benchmarks.