Semantic object recognition by merging decision tree with object ontology

Damak, Wafa; Kallel, Imène Khanfir

doi:10.1109/atsip.2014.6834667

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The classification rules were used to create the ontology inference rules; then, the developed ontology could identify the learner factors that inferred the likelihood of students being successful. Damak et al 68 proposed an object recognition method that identified household objects to provide information to visually impaired people. They used a decision tree and SVM to classify the low‐level features that were extracted from the object images.…”

Section: Knowledge‐based and Data‐mining Processesmentioning

confidence: 99%

Semantic data mining in the information age: A systematic review

Chanmee

Kesorn

2021

Int J Intell Syst

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Data mining is the discovery of meaningful information or unrevealed patterns in data. Traditional data‐mining approaches, using statistical calculations, machine learning, artificial intelligence, and database technology, cannot interpret data on a conceptual or semantic level and fail to reveal the meanings within the data. This results in a user not being analyzed and determines its signification and implications. Several semantic data‐mining approaches have been proposed in the past decade that overcome these limitations by using a domain ontology as background knowledge to enable and enhance data‐mining performance. The main contributions of this literature survey include organizing the surveyed articles in a new way that provides ease of understanding for interested researchers, and the provision of a critical analysis and summary of the surveyed articles, identifying the contribution of these papers to the field, and the limitations of the analysis methods and approaches discussed in this corpus, with the intention of informing researchers in this growing field in their innovative approaches to new research. Finally, we identify the future trends and challenges in this study track that will be of concern to future researchers, such as dynamic knowledge‐based methods or big‐data tool collaboration. This survey article provides a comprehensive overview of the literature on domain ontologies as used in the various semantic data‐mining tasks, such as preprocessing, modeling, and postprocessing. We investigated the role of semantic data mining in the field of data science and the processes and methods of applying semantic data mining to a data resource description framework.

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Section: Knowledge‐based and Data‐mining Processesmentioning

confidence: 99%

Semantic data mining in the information age: A systematic review

Chanmee

Kesorn

2021

Int J Intell Syst

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A Language-Independent Ontology Construction Method Using Tagged Images in Folksonomy

2018

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Improved Object Recognition with Decision Trees Using Subspace Clustering

Peralta¹,

Alberto²

2016

J. Adv. Comput. Intell. Intell. Inform.

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Generic object recognition algorithms usually require complex classificationmodels because of intrinsic difficulties arising from problems such as changes in pose, lighting conditions, or partial occlusions. Decision trees present an inexpensive alternative for classification tasks and offer the advantage of being simple to understand. On the other hand, a common scheme for object recognition is given by the appearances of visual words, also known as the bag-of-words method. Although multiple co-occurrences of visual words are more informative regarding visual classes, a comprehensive evaluation of such combinations is unfeasible because it would result in a combinatorial explosion. In this paper, we propose to obtain the multiple co-occurrences of visual words using a variant of the CLIQUE subspace-clustering algorithm for improving the object recognition performance of simple decision trees. Experiments on standard object datasets show that our method improves the accuracy of the classification of generic objects in comparison to traditional decision tree techniques that are similar, in terms of accuracy, to ensemble techniques. In future we plan to evaluate other variants of decision trees, and apply other subspace-clustering algorithms.

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Semantic object recognition by merging decision tree with object ontology

Cited by 3 publications

References 13 publications

Semantic data mining in the information age: A systematic review

Semantic data mining in the information age: A systematic review

A Language-Independent Ontology Construction Method Using Tagged Images in Folksonomy

Improved Object Recognition with Decision Trees Using Subspace Clustering

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