@conference {cVaras14, title = {Region-based Particle Filter for Video Object Segmentation}, booktitle = {CVPR - Computer Vision and Pattern Recognition}, year = {2014}, publisher = {IEEE}, organization = {IEEE}, address = {Ohio}, abstract = {

We present a video object segmentation approach that\ extends the particle filter to a region-based image representation. Image partition is considered part of the particle filter measurement, which enriches the available information and leads to a re-formulation of the particle filter.\ The prediction step uses a co-clustering between the previous image object partition and a partition of the current\ one, which allows us to tackle the evolution of non-rigid\ structures. Particles are defined as unions of regions in the\ current image partition and their propagation is computed\ through a single co-clustering. The proposed technique is\ assessed on the SegTrack dataset, leading to satisfactory\ perceptual results and obtaining very competitive pixel error rates compared with the state-of-the-art methods.

}, keywords = {co-clustering, particle filter, segmentation, tracking}, author = {David Varas and Marqu{\'e}s, F.} }