Title

An energy-based model for region-labeling

Author

Hugo Jair Escalante Balderas

Manuel Montes y Gómez

Luis Enrique Sucar Succar

Access level

Open Access

Summary or description

This paper introduces an energy-based model (EBM) for region labeling that takes advantage of both context and semantics present in segmented images.The proposed method refines the output of multiclass classification methods that are based on the one-vs-all (OVA) formulation. Intuitively, the EBM maximizes the semantic cohesion among labels assigned to neighboring regions; that is, a tradeoff between label-association information and the predictions from the base classifier. Additionally, we study the suitability of OVA classification for the region labeling task. We report experimental results of our methods in 12 heterogeneous data sets that have been used for the evaluation of different tasks besides region labeling. On the one hand, our results reveal that the OVA approach offers an important potential of improvement in terms of labeling performance that can be exploited by refinement techniques similar to ours. On the other hand, experimental results show that our EBM improves the labeling provided by the base classifier. The EBM is highly efficient and it can be applied without modifications to different data sets. The heterogeneity of the considered databases shows the generality of our approach and its robustness to different scenarios. Our results are superior to other techniques that have been tested in the same collections. Furthermore, results on image retrieval show that the labels generated with our EBM can be helpful for annotation-based image retrieval.

Publisher

Elsevier Inc.

Publish date

2011

Publication type

Article

Publication version

Accepted Version

Format

application/pdf

Language

English

Audience

Students

Researchers

General public

Citation suggestion

Escalante-Balderas, H.J., et al., (2011). An energy-based model for region-labeling, Computer Vision and Image Understanding, (115): 787–803

Source repository

Repositorio Institucional del INAOE

Downloads

73

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