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RISS 인기검색어
- 검색키워드
- 저자 : Mohamed Maher Ben Ismail (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Automatic Image Annotation using Possibilistic Clustering Algorithm
Mohamed Maher Ben Ismail,Sara N. Alfaraj,Ouiem Bchir 한국지능시스템학회 2019 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.19 No.4
In this paper, the proposed PCMRM (possibilistic based cross-media relevance model) annotates images based on their visual contents. PCMRM framework relies on unsupervised learning to group the visually similar image regions into homogeneous clusters, along with the cross-media relevance model (CMRM) that is used to estimate the joint distribution of textual keywords and images. Besides, the unsupervised learning task exploits the robustness to noise of a possibilistic clustering algorithm, and generates membership degrees that represent the typicality of image regions with respect to the obtained clusters. To validate and assess the proposed system, we used the standard Corel dataset. PCMRM produced promising results. The reported performance measures proved that the proposed automatic image annotation approach outperforms similar state of the art solutions. This attainment is mainly attributed to the exploitation of the possibilistic membership produced by the clustering algorithm which allowed accurate learning of the association between annotating labels and the visual content of the image regions.
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Region-Based Image Retrieval Using Relevance Feature Weights
Ouiem Bchir,Mohamed Maher Ben Ismail,Hadeel Aljam 한국지능시스템학회 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.1
We propose a new region-based CBIR (content-based image retrieval) system. One of the main objectives of our work is to reduce the semantic gap between the visual characteristics of the query and the high level semantic sought by the user. This is achieved by allowing the user to select specific regions and expressing his interest in a more accurate way. Moreover, the proposed approach overcomes the challenge of choosing suitable features to describe the image content. More specifically, relevance weights are automatically associated with each visual feature in order to better represent the visual content of the images. To evaluate these objectives, we compare the obtained results with those obtained using traditional CBIR systems.
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Region-Based Image Retrieval Using Relevance Feature Weights
Bchir, Ouiem,Ben Ismail, Mohamed Maher,Aljam, Hadeel Korean Institute of Intelligent Systems 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.1
We propose a new region-based CBIR (content-based image retrieval) system. One of the main objectives of our work is to reduce the semantic gap between the visual characteristics of the query and the high level semantic sought by the user. This is achieved by allowing the user to select specific regions and expressing his interest in a more accurate way. Moreover, the proposed approach overcomes the challenge of choosing suitable features to describe the image content. More specifically, relevance weights are automatically associated with each visual feature in order to better represent the visual content of the images. To evaluate these objectives, we compare the obtained results with those obtained using traditional CBIR systems.
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Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering
Norah Ibrahim Fantoukh,Mohamed Maher Ben Ismail,Ouiem Bchir 한국지능시스템학회 2020 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.20 No.2
Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering process towards optimal categorization and escape from local minima. In this paper, we propose a novel fuzzy relational semi-supervised clustering algorithm based on an adaptive local distance measure (SSRF-CA). The proposed clustering algorithm utilizes side-information and formulates it as a set of constraints to supervise the learning task. These constraints are expressed using reward and penalty terms, which are integrated into a novel objective function. In particular, we formulate the clustering task as an optimization problem through the minimization of the proposed objective function. Solving this optimization problem provides the optimal values of different objective function parameters and yields the proposed semi-supervised clustering algorithm. Along with its ability to perform data clustering and learn the underlying dissimilarity measure between the data instances, our algorithm determines the optimal number of clusters in an unsupervised manner. Moreover, the proposed SSRF-CA is designed to handle relational data. This makes it appropriate for applications where only pairwise similarity (or dissimilarity) information between data instances is available. In this paper, we proved the ability of the proposed algorithm to learn the appropriate local distance measures and the optimal number of clusters while partitioning the data using various synthetic and real-world benchmark datasets that contain varying numbers of clusters with diverse shapes. The experimental results revealed that the proposed SSRF-CA accomplished the best performance among other state-of-the-art algorithms and confirmed the outperformance of our clustering approach.
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