A Relevant Visual Feature Selection Approach for Image Retrieval

Allani, Olfa; Mellouli, Nédra; Zghal, Hajer Baazaoui; Akdag, Herman; Ghzala, Henda Ben

doi:10.5220/0005306303770384

Cited by 4 publications

(2 citation statements)

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“…Many image retrieval systems have been developed, such as Text-based Image Retrieval (TBIR) [24], Content-based Image Retrieval (CBIR [8,10]). These systems which retrieve images by keywords, text or visual contents still lack the semantic analysis of images [1,3], so the search results usually return the images unrelated, performance of image retrieval is still far from user's expectations. To overcome the above disadvantages in TBIR and CBIR, semantic based image retrieval (SBIR) is proposed.…”

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confidence: 99%

“…The contributions of the paper include: (1) building an automatic clustering model by proposing a self-balanced clustering tree structure (C-Tree) to store low-level visual content of the images; (2) proposing model and algorithms of SBIR CT to retrieve semantics of similar images; (3) building ontology for image dataset on the basis of triple language RDF (Resource Description Framework) [16,17] and creating a SPARQL command [31,32] to retrieve similar images based on visual word vector; (4) constructing the SBIR CT system based on proposed model and algorithms to implement the evaluation on ImageCLEF dataset.…”

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A Self-Balanced Clustering Tree for Semantic-Based Image Retrieval

Nhi

Van

Thạnh

2020

JCC

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The image retrieval and semantic extraction play an important role in the multimedia systems such as geographic information system, hospital information system, digital library system, etc. Therefore, the research and development of semantic-based image retrieval (SBIR) systems have become extremely important and urgent. Major recent publications are included covering different aspects of the research in this area, including building data models, low-level image feature extraction, and deriving high-level semantic features. However, there is still no general approach for semantic-based image retrieval (SBIR), due to the diversity and complexity of high-level semantics. In order to improve the retrieval accuracy of SBIR systems, our focus research is to build a data structure for finding similar images, from that retrieving its semantic. In this paper, we proposed a data structure which is a self-balanced clustering tree named C-Tree. Firstly, a method of visual semantic analysis relied on visual features and image content is proposed on C-Tree. The building of this structure is created based on a combination of methods including hierarchical clustering and partitional clustering. Secondly, we design ontology for the image dataset and create the SPARQL (SPARQL Protocol and RDF Query Language) query by extracting semantics of image. Finally, the semantic-based image retrieval on C-Tree (SBIR\_CT) model is created hinging on our proposal. The experimental evaluation 20,000 images of ImageCLEF dataset indicates the effectiveness of the proposed method. These results are compared with some of recently published methods on the same dataset and demonstrate that the proposed method improves the retrieval accuracy and efficiency.

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A Self-Balanced Clustering Tree for Semantic-Based Image Retrieval

Nhi

Van

Thạnh

2020

JCC

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A Knowledge-based Image Retrieval System Integrating Semantic and Visual Features

Allani

Zghal

Mellouli

et al. 2016

Procedia Computer Science

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How to compare search engines in CBIR?

Jaworska

2016

2016 SAI Computing Conference (SAI)

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At present a great deal of research is being done in different aspects of Content-Based Image Retrieval (CBIR) of which the search engine is one of the most important clements. In this paper we cover the state-of-the-art techniques in CBIR according to the aims of retrieval and matching techniques. The issue we address is the analysis of search engines reducing the 'semantic gap'. The matching methods are compared in terms of their usefulness for different user's aims. Finally, we compare our search engine with Google's and the SIFf method.

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A Relevant Visual Feature Selection Approach for Image Retrieval

Cited by 4 publications

References 10 publications

A Self-Balanced Clustering Tree for Semantic-Based Image Retrieval

A Self-Balanced Clustering Tree for Semantic-Based Image Retrieval

A Knowledge-based Image Retrieval System Integrating Semantic and Visual Features

How to compare search engines in CBIR?

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