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An energy-based model for region-labeling
Hugo Jair Escalante Balderas
Manuel Montes y Gómez
Luis Enrique Sucar Succar
Acceso Abierto
Region labeling
Energy-based modeling
Random forest
Image annotation
Object recognition
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.
Elsevier Inc.
Público en general
Escalante-Balderas, H.J., et al., (2011). An energy-based model for region-labeling, Computer Vision and Image Understanding, (115): 787–803
Versión aceptada
acceptedVersion - Versión aceptada
Appears in Collections:Artículos de Ciencias Computacionales

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