An energy-based model for region-labeling

Montes-y-Gómez, Manuel; Sucar, L. Enrique

doi:10.1016/j.cviu.2011.02.002

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…The EBM‐based object categorisation model proposed in this work overcomes the defects of the related works which rely on the use of undirected graphical models and often lead to intractable partition functions [4, 12]. We did more sufficiently consider the spatial arrangement than the EBMs [16, 19] similar to this work.…”

Section: Introductionmentioning

confidence: 99%

“…Here, they used only four fixed spatial relations such as Above, Below, Inside and Around, and approximated the partition function by using the Monte Carlo integration. In contrast, Escalante et al [16] and Papadopoulos et al [18] introduced the EBM-based models which avoid the evaluation of intractable partition function. In Escalante et al, the energy function was defined as integrating the appearance-based observation and the simple concept co-occurrence statistics.…”

Section: Figure6 Comparison Of Object Categorization Accuracies For mentioning

confidence: 99%

“…In order to verify the effectiveness of the proposed method, we compared the object categorization results with several state-of-the-art baselines, namely Galleguillos et al [12], Escalante et al [16] and Papadopoulos et al [18] for SCEF, MSRC v2 and PASCAL VOC2010 image datasets. Here, we adopted the MPEG-7 and the SIFT descriptors which have been used in the baselines as visual features extracted from each segmented region.…”

Section: Figure6 Comparison Of Object Categorization Accuracies For mentioning

confidence: 99%

“…Minimizing the energy function of whole image arrangement, we obtain the optimal label set about the image regions without the evaluation of intractable partition function in the probabilistic graphical models. Compared with the existing contextual object categorization models such as the literatures [3,16], where the co-occurrence have merely used, and the literatures [12,14], where have only used the fixed spatial relations and considered the simple frequency counting, we investigate the spatial arrangements in an image more sufficiently by defining fuzzy contextual relations. Moreover, the proposed EBM-based object categorization model overcomes the defects of the related works which rely on the use of undirected graphical models and often lead to intractable partition functions [3,12].…”

Section: Introductionmentioning

confidence: 99%

“…However, there is an evaluation of intractable partition function for normalization of probabilistic models. Energy-based models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables [15,16,18]. It allows us to relax the strict probabilistic modeling and to address the evaluation problem of intractable partition function.…”

Section: Introductionmentioning

confidence: 99%

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Contextual object categorisation with energy‐based model

Ri¹,

DuHo²,

Choe³

et al. 2017

J. eng.

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：Object categorization is a hot issue of an image mining. Contextual information between objects is one of the important semantic knowledge of an image. However, the previous researches for an object categorization have not made full use of the contextual information, especially the spatial relations between objects. In addition, the object categorization methods, which generally use the probabilistic graphical models to implement the incorporation of contextual information with appearance of objects, are almost inevitable to evaluate the intractable partition function for normalization. In this work, we introduced fully-connected fuzzy spatial relations including directional, distance and topological relations between object regions, so the spatial relational information could be fully utilized. Then, the spatial relations were considered as well as co-occurrence and appearance of objects by using energy-based model, where the energy function was defined as the region-object association potential and the configuration potential of objects. Minimizing the energy function of whole image arrangement, we obtained the optimal label set about the image regions and addressed the evaluation of intractable partition function in conditional random fields. Experimental results show the validity and reliability of this proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Figure6 Comparison Of Object Categorization Accuracies For mentioning

confidence: 99%

Section: Figure6 Comparison Of Object Categorization Accuracies For mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Contextual object categorisation with energy‐based model

Ri¹,

DuHo²,

Choe³

et al. 2017

J. eng.

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Evaluating Term-Expansion for Unsupervised Image Annotation

Pellegrin

Montes-y-Gómez

2014

Lecture Notes in Computer Science

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Multimodal indexing based on semantic cohesion for image retrieval

Montes

Sucar

2011

Inf Retrieval

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This paper introduces two novel strategies for representing multimodal images with application to multimedia image retrieval. We consider images that are composed of both text and labels: while text describes the image content at a very high semantic level (e.g., making reference to places, dates or events), labels provide a mid-level description of the image (i.e., in terms of the objects that can be seen in the image). Accordingly, the main assumption of this work is that by combining information from text and labels we can develop very effective retrieval methods. We study standard information fusion techniques for combining both sources of information. However, whereas the performance of such techniques is highly competitive, they cannot capture effectively the content of images. Therefore, we propose two novel representations for multimodal images that attempt to exploit the semantic cohesion among terms from different modalities. Such representations are based on distributional term representations widely used in computational linguistics. Under the considered representations the content of an image is modeled by a distribution of co-occurrences over terms or of occurrences over other images, in such a way that the representation can be considered an expansion of the multimodal terms in the image. We report experimental results using the SAIAPR TC12 benchmark on two sets of topics used in ImageCLEF competitions with manually and automatically generated labels. Experimental results show that the proposed representations outperform significantly both, standard multimodal techniques and unimodal methods. Results on manually assigned labels provide an upper bound in the retrieval performance that can be obtained, whereas results with automatically generated labels are encouraging. The novel representations are able to capture more effectively the content of multimodal images. We emphasize that although we have applied our representations to multimedia image retrieval the same formulation can be adopted for modeling other multimodal documents (e.g., videos).

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An energy-based model for region-labeling

Cited by 13 publications

References 37 publications

Contextual object categorisation with energy‐based model

Contextual object categorisation with energy‐based model

Evaluating Term-Expansion for Unsupervised Image Annotation

Multimodal indexing based on semantic cohesion for image retrieval

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