Modular ontologies and CBR-based hybrid system for web information retrieval

Besbes, Ghada; Baazaoui-Zghal, Hajer

doi:10.1007/s11042-014-2041-z

Cited by 13 publications

(2 citation statements)

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“…Ontologies are powerful knowledge representation tools that provide an explicit specification of knowledge in a structured and organized manner. They allow enrichment and direct access to several types of relationships between the different concepts (Besbes and Baazaoui Zghal, 2014). Thus, we propose to model three ontologies: image semantic content, meta-data and low-level features.…”

Section: Visual Features Meta-data and Image Content Ontologiesmentioning

confidence: 99%

A Relevant Visual Feature Selection Approach for Image Retrieval

Allani

Mellouli²,

Zghal

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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Content-Based Image Retrieval approaches have been marked by the semantic gap (inconsistency) between the perception of the user and the visual description of the image. This inconsistency is often linked to the use of predefined visual features randomly selected and applied whatever the application domain. In this paper we propose an approach that adapts the selection of visual features to semantic content ensuring the coherence between them. We first design visual and semantic descriptive ontologies. These ontologies are then explored by association rules aiming to link semantic descriptor (a concept) to a set of visual features. The obtained feature collections are selected according to the annotated query images. Different strategies have been experimented and their results have shown an improvement of the retrieval task based on relevant feature selections.

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Section: Visual Features Meta-data and Image Content Ontologiesmentioning

confidence: 99%

A Relevant Visual Feature Selection Approach for Image Retrieval

Allani

Mellouli²,

Zghal

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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“…A classification model can be realized by means of a number of methods, from statistical methods (a cluster analysis) to computational intelligence methods (neural networks, fuzzy inference system etc.). One of possible approaches is also the use of case-based reasoning (CBR) [1], [3], [4], [6], [7], [14], [16], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Classification of Clients on the Basis of Modifying Case-Based Reasoning Algorithms

Mezera

Křupka

2017

Advances in Intelligent Systems and Computing

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This article deals with client's classification possibilities for trade companies. One of the options for dealing with classification is to use Case-based Reasoning method. The proposed modified algorithm clears cases base, it eliminates invalid data and out-dated data from the time point of view. Hill-climbing algorithm is used for this. Classification results achieved by means of modified algorithm are compared with other classification methods such as Neuron Networks, Top Down Induction Decision Trees and Logistic Regression.

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