Ontology-based approach for information fusion

Boury-Brisset, Anne-Claire

doi:10.1109/icif.2003.177491

Cited by 59 publications

(36 citation statements)

References 6 publications

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“…In plain language this means that the above equations indeed result in conceptmodels in the "mind" of the MFT system, which are most similar [in terms of similarity (1)] to the sensory data. Despite a combinatorially large number of items in (1), a computational complexity of the MF method is relatively low, it is linear in N. This theorem is proved by demonstrating that similarity (1) increases at each iteration [5].…”

Section: Modeling Field Theory (Mft)mentioning

confidence: 99%

Evolving Agents: Communication and Cognition

Perlovsky

2005

Lecture Notes in Computer Science

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Section: Modeling Field Theory (Mft)mentioning

confidence: 99%

Evolving Agents: Communication and Cognition

Perlovsky

2005

Lecture Notes in Computer Science

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“…As illustrated in Example 1, the POIs under consideration are objects of a complex 6 that consists of six non compound universa U 1 , . .…”

Section: Illustration Of Coreference Detectionmentioning

confidence: 99%

Quality Issues in the Management of Web Information

Pasi¹,

Bordogna²,

Jain³

2013

Intelligent Systems Reference Library

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In the context of geographic information systems (GIS), points of interest (POIs) are descriptions that denote geographical locations which might be of interest for some user purposes. Examples are public transport facilities, historical buildings, hotels and restaurants, recreation areas, hospitals etc. Because information gathering with respect to POIs is usually resource consuming, the user community is often involved in this task. In general, POI data originate from different sources (or users) and are therefore vulnerable to imperfections which might have a negative impact on data quality. Different POIs referring to, or describing the same physical geographical location might exist. Such POIs are said to be coreferent POIs. Coreferent POIs must be avoided as they could harm the data(base) quality and integrity. In this chapter, a novel soft computing technique for the (semi-)automated cleansing of POI databases is proposed. The proposed technique consists of two consecutive main steps: the detection of collections of coreferent POIs and the fusion, for each collection, of all coreferent POIs into a single consistent POI that represents all the POIs in the collection. The technique is based on fuzzy set theory, whereas possibility theory is used to cope with the uncertainties in the data. It can be used as a component of (semi-)automated data quality improvement strategies for databases and other information sources.

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“…They have pre-computed the cost adjacency matrix and its Eigenvectors corresponding to the smallest Eigenvalues and then applied the Riemannian Manifolds learning concepts to estimate the boundary between positive and negative images. Claire and Brisset [10] exhibited an ontological way to deal with the mapping between highlevel and low-level information, and it is applied to heterogeneous information integration for the configuration of a knowledge server supporting Situation/Threat Assessment and Resource Management (STA/RM) processes. The building of ontology is timeconsuming and should be supported by methodologies and tools.…”

Section: Literature Surveymentioning

confidence: 99%

An Interactive Approach for Retrieval of Semantically Significant Images

Mane¹,

Rathi²,

Bawane³

2016

IJIGSP

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Abstract-Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between lowlevel primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.Index Terms-Semantic gap, content-based image retrieval, relevance feedback, HSV histogram, local binary pattern, color coherence vector.

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Ontology-based approach for information fusion

Cited by 59 publications

References 6 publications

Evolving Agents: Communication and Cognition

Evolving Agents: Communication and Cognition

Quality Issues in the Management of Web Information

An Interactive Approach for Retrieval of Semantically Significant Images

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