Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation
This addresses the problem of accurate semantic segmentation in videos for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles semantic video object segmentation by proposing a graph-based model that learns and propagates higher-level spatial-temporal contextual relationships to label local regions, and shows it outperforms state-of-the-art methods on the YouTube-Objects dataset.
We propose a novel approach for modeling semantic contextual relationships in videos. This graph-based model enables the learning and propagation of higher-level spatial-temporal contexts to facilitate the semantic labeling of local regions. We introduce an exemplar-based nonparametric view of contextual cues, where the inherent relationships implied by object hypotheses are encoded on a similarity graph of regions. Contextual relationships learning and propagation are performed to estimate the pairwise contexts between all pairs of unlabeled local regions. Our algorithm integrates the learned contexts into a Conditional Random Field (CRF) in the form of pairwise potentials and infers the per-region semantic labels. We evaluate our approach on the challenging YouTube-Objects dataset which shows that the proposed contextual relationship model outperforms the state-of-the-art methods.