The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Context-Based Scene Understanding
Abstract
Context plays an important role in performance of object detection. There are two popular considerations in building context models for computer vision applications; type of context (semantic, spatial, scale) and scope of the relations (pairwise, high-order). In this paper, a new unified framework is presented that combines multiple sources of context in high-order relations to encode semantical coherence and consistency of the scenes. This framework introduces a new descriptor called context relevance score to model context-based distribution of the response variables and apply it to two distributions. First model incorporates context descriptor along with annotation response into a supervised Latent Dirichlet Allocation (LDA) built on multi-variate Bernoulli distribution called Context-Based LDA (CBLDA). The second model is based on multi-variate Wallenius' non-central Hyper-geometric distribution and is called Wallenius LDA (WLDA). WLDA incorporates context knowledge as bias parameter. Scene context is modeled as a graph and effectively used in object detection framework to maximize semantical consistency of the scene. The graph can also be used in recognition of out-of-context objects. Annotation metadata of Sun397 dataset is used to construct the context model. Performance of the proposed approaches was evaluated on ImageNet dataset. Comparison between proposed approaches and state-of-art multi-class object annotation algorithm shows superiority of presented approach in labeling of scene content.
Related Content
|
Jayashri Dutta, Smitakshi Medhi, Mayurakshi Gogoi, Lisha Borgohain, Nourhan Gamal Abdel Maboud, Hanaa Mustafa Muhameed.
© 2025.
34 pages.
|
|
Abdellah Khouz, Jorge Trindade, Fatima El Bchari, Pedro Pinto Santos, Eusébio Reis, Adil Moumane, Fatima Ezzahra El Ghazali, Mourad Jadoud, Blaid Bougadir.
© 2025.
38 pages.
|
|
Phyo Thandar Hlaing, Muhammad Waqas, Usa Wannasingha Humphries.
© 2025.
32 pages.
|
|
Adil Moumane, Jamal Al Karkouri, Batchi Mouhcine.
© 2025.
28 pages.
|
|
Abdessamad Elmotawakkil, Nourddine Enneya.
© 2025.
20 pages.
|
|
Fatima Ezzahra El Ghazali, Abdellah Khouz.
© 2025.
30 pages.
|
|
Tarik Bahouq, Amina Moumane, Nadia Touhami.
© 2025.
28 pages.
|
|
|