Abstract
Extended from superpixel segmentation by adding an additional constraint on temporal consistency, supervoxel segmentation is to partition video frames into atomic segments. In this work, we propose a novel scheme for supervoxel segmentation to address the problem of new and moving objects, where the segmentation is performed on every two consecutive frames and thus each internal frame has two valid superpixel segmentations. This scheme provides coarse-grained parallel ability, and subsequent algorithms can validate their result using two segmentations that will further improve robustness. To implement this scheme, a voxel-related Gaussian mixture model (GMM) is proposed, in which each supervoxel is assumed to be distributed in a local region and represented by two Gaussian distributions that share the same color parameters to capture temporal consistency. Our algorithm has a lower complexity with respect to frame size than the traditional GMM. According to our experiments, it also outperforms the state-of-the-art in accuracy.
Highlights
Superpixel segmentation is to partition a still image into atomic segments of similar size and adhering to object boundaries, namely superpixels [1,2,3,4]
A temporal superpixel algorithm was developed based on our novel voxel-related Gaussian mixture model (GMM)
Instead of producing immutable superpixels for each frame, we proposed a new scheme for supervoxel segmentation
Summary
Superpixel segmentation is to partition a still image into atomic segments of similar size and adhering to object boundaries, namely superpixels [1,2,3,4]. Methods falling into the first category cluster video pixels in 3D Euclidean space with color information added to each point This strategy may result in supervoxels only preserving temporal consistency in very few neighboring frames. Because the segmentations of the previous frames are immutable, the seeds that were used to initialize superpixels of the current frame may change to a different object due to occlusions, and the temporal consistency is lost (Figure 1a illustrates this problem). We use the same color parameters (i.e., color mean vector and color covariance matrix) for the two distributions of each supervoxel This is mainly because the same object in two consecutive frames tends to be similar in color.
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