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Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation

Tinghuai Wang, Huiling Wang

TL;DR

The paper tackles semantic video object segmentation under challenging motion and appearance variations by learning and propagating higher-level semantic contexts without requiring training data. It introduces an exemplar-based nonparametric approach that builds a similarity graph over regions derived from trajectory hypotheses, uses a link-prediction viewpoint to propagate context, and integrates the results into a fully connected CRF for accurate per-region labeling. Key contributions include a two-pass context propagation strategy to estimate cross-object relationships and a CRF-based inference framework that leverages learned context scores. On the YouTube-Objects benchmark, the method achieves state-of-the-art performance, demonstrating the value of global contextual reasoning for robust video segmentation.

Abstract

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.

Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation

TL;DR

The paper tackles semantic video object segmentation under challenging motion and appearance variations by learning and propagating higher-level semantic contexts without requiring training data. It introduces an exemplar-based nonparametric approach that builds a similarity graph over regions derived from trajectory hypotheses, uses a link-prediction viewpoint to propagate context, and integrates the results into a fully connected CRF for accurate per-region labeling. Key contributions include a two-pass context propagation strategy to estimate cross-object relationships and a CRF-based inference framework that leverages learned context scores. On the YouTube-Objects benchmark, the method achieves state-of-the-art performance, demonstrating the value of global contextual reasoning for robust video segmentation.

Abstract

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.
Paper Structure (9 sections, 4 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 9 sections, 4 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. The Approach
  3. Trajectory Hypotheses
  4. Graph Construction
  5. Context Modeling
  6. Context Prediction
  7. Inference
  8. Experiments
  9. Conclusion

Figures (2)

  • Figure 1: Illustration of the proposed approach. Two trajectory hypotheses are extracted to provide initial annotations for 'horse' and 'person' classes, which form the context exemplars such as (horse, person)-link between the corresponding vertices on the similarity graph. Our method propagates such contextual relationship on the graph and predicts the probability of (horse, person)-link between unlabeled vertices based on similarity.
  • Figure 2: Qualitative results of our algorithm on YouTube-Objects Dataset.