The use of contextual spatial knowledge for low-quality image segmentation

Kallel, Imène Khanfir; Almouahed, Shaban; Alsahwa, Bassem; Solaiman, Basel

doi:10.1007/s11042-018-6540-1

Cited by 2 publications

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“…Table 16 for the impoverished datasets, show the advantage of the possibility theory in data modeling compared to the probability theory and the SVM classifier in poor data environments (information incompleteness). Same kind of advantages have been confirmed from results that have been obtained for different kind of applications namely in pattern recognition and image segmentation in the processing of poor-quality images [32,[34][35][36][37].…”

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An intelligent quality-based approach to fusing multi-source possibilistic information

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Recently, Yager and Petry were proposing a quality-based methodology to combine data provided by multiple probabilistic sources to improve the quality of information for decision-makers. This paper offers a sort of companion paper that adapts this methodology to possibilistic sources. Possibility theory is particularly well suited to cope with incomplete information from poor-data sources. The methodology and algorithms used for the probabilistic approach are adapted for the possibilistic case. Both approaches are then compared by the means of a numerical example and four experimental benchmark datasets: one, the IRIS data set, being data-poorer than the three other ones (Diabetes dataset, Glass dataset and Liver-disorder dataset). A vector representation is introduced for a possibility distribution as in the probabilistic case and, the Gini's formulation of entropy is being used. However, the Gini's entropy has to be used differently than with the probabilistic case. This has an impact on the selection of subsets. A fusion scheme is designed to select the best-quality subsets according to two information quality factors: quantity of information and source credibility. Results obtained from comparison of both approaches on the four experimental benchmarks confirm the superiority of the possibilistic approach in the presence of information scarcity or incompleteness.

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An intelligent quality-based approach to fusing multi-source possibilistic information

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“…Although, several recent works have been done on the applications of similarity measures [27]- [32] in different fields of research, relatively few ones are dedicated to measure the similarity between possibility distributions.…”

Section: B Existing Possibilistic Similarity Measuresmentioning

confidence: 99%

Possibilistic Similarity Measures for Data Science and Machine Learning Applications

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Measuring similarity is of a great interest in many research areas such as in data sciences, machine learning, pattern recognition, text analysis and information retrieval to name a few. Literature has shown that possibility is an attractive notion in the context of distinguishability assessment and can lead to very efficient and computationally inexpensive learning schemes. This paper focuses on determining the similarity between two possibility distributions. A review of existing similarity measures within the possibilistic framework is presented first. Then, similarity measures are analyzed with respect to their capacity to satisfy a set of required properties that a similarity measure should own. Most of the existing possibilistic similarity measures produce undesirable outcomes since they generally depend on the application context. A new similarity measure, called InfoSpecificity, is introduced and the similarity measures are categorized into three main methods: morphic-based, amorphic-based and hybrid. Two experiments are being conducted using four benchmark databases. The aim of the experiments is to compare the efficiency of the possibilistic similarity measures when applied to real data. Empirical experiments have shown good results for the hybrid methods, particularly with the InfoSpecificity measure. In general, the hybrid methods outperform the other two categories when evaluated on small-size samples, i.e., poor-data context (or poor-informed environment) where possibility theory can be used at the greatest benefit.

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The use of contextual spatial knowledge for low-quality image segmentation

Cited by 2 publications

References 18 publications

An intelligent quality-based approach to fusing multi-source possibilistic information

An intelligent quality-based approach to fusing multi-source possibilistic information

Possibilistic Similarity Measures for Data Science and Machine Learning Applications

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