| Autor: |
Wang, Hao, Lu, Tong, Wang, Yiming, Shivakumara, Palaiahnakote, Tan, Chew |
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| Zdroj: |
Multimedia Tools & Applications; Mar2016, Vol. 75 Issue 6, p3027-3051, 25p |
| Abstrakt: |
Scene image understanding has drawn much attention for its intriguing applications in the past years. In this paper, we propose a unified probabilistic graphical model called Topic-based Coherent Region Annotation (TCRA) for weakly-supervised scene region annotation. The multiscale over-segmented regions within a scene image are considered as the 'words' of our topic model, which impose neighborhood contextual constraints on topic level through spatial MRF modeling, and incorporate an annotation reasoning mechanism for learning and inferring region labels automatically. Mean field variational inference is provided for model learning. The proposed TCRA has the following two main advantages for understanding natural scene images. First, spatial information of multiscale over-segmented regions is explicitly modeled to obtain coherent region annotations. Second, only image-level labels are needed for automatically inferring the label of every region within the scene. This is particularly helpful in reducing human burden on manually labeling pixel-level semantics in the scene understanding research. Thus, given a scene image that has no textual prior, the regions in it can be automatically labeled using the learned TCRA model. The experimental results conducted on three benchmarks consisting of the MSRCORID image dataset, the UIUC Events image dataset and the SIFT FLOW dataset show that the proposed model outperforms the recent state-of-the-art methods. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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