Pattern graph-based image retrieval system combining semantic and visual features

Allani, Olfa; Zghal, Hajer Baazaoui; Mellouli, Nédra; Akdag, Herman

doi:10.1007/s11042-017-4716-8

Cited by 7 publications

(13 citation statements)

References 26 publications

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“…Mean Average Precision (MAP) H. Cevikalp, 2017 [5] 0.4678 O. Allani, 2017 [2] 0.3460 M. Jiu, 2017 [13] 0.5970 Y. Cao, 2016 [4] 0.7236 SBIR CT 0.6510…”

Section: Methodsmentioning

confidence: 99%

“…Semantic-based image retrieval has become an active research topic in recent times. There were many techniques of image retrieval, which have been implemented aiming to reduce the "semantic gap" by modeling high-level semantics, such as techniques to build a model for mapping between low-level features and high-level semantics [2,21], query techniques based on ontology to accurately describe semantics for images [18,25], techniques for classification data [12,13,17], etc.…”

Section: Related Workmentioning

confidence: 99%

“…In 2017, Allani Olfa et al [2] proposed pattern-based image retrieval system SemVisIR, which combined semantic and visual features. They organized the image dataset in a graph of patterns which are automatically built for the different domains by clustering algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…To overcome the above disadvantages in TBIR and CBIR, semantic based image retrieval (SBIR) is proposed. SBIR extracts features to identify meaning of images; then, it retrieves the related images in visual features and extracts semantics of contents of these images [2,12,23]. There are two challenges with this approach.…”

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confidence: 99%

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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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“…Mean Average Precision (MAP) H. Cevikalp, 2017 [5] 0.4678 O. Allani, 2017 [2] 0.3460 M. Jiu, 2017 [13] 0.5970 Y. Cao, 2016 [4] 0.7236 SBIR CT 0.6510…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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

Nhi

Van

Thạnh

2020

JCC

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“…The major part of the big data consists of heterogeneous multimedia content such as text, image, audio, video, these are all increasing rapidly [1]. Many has been conducted in the content based media retrieval and the prevailing methods are single-media retrieval and multi-modal retrieval [2].…”

Section: Introductionmentioning

confidence: 99%

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Ayyavaraiah¹,

Venkateswarlu²

2018

IJET

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The large number of heterogeneous data are rapidly increasing in the internet and most data consist of audio, video, text and images. The searching of the required data from the large database is difficult and time taking process. The single media retrieval is used to get the needed data from the large dataset and it has the drawback, it can only retrieve the single media only. If the query is given as the text and acquired result are present in text. The users demand the cross-media retrieval for their queries and it is very consistent in providing the result. This helps the users to get more information regarding to their queries. Finding the similarities between the heterogeneous data is very complex. Many research is done on the cross-media retrieval with different methods and provide the different result. The aim is to analysis the different cross-media retrieval with the joint graph regularization (JGR) to understand the various technique. The most of researches are using the parameter of MAP, precision and recall for their research.

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Improving the Efficiency of Semantic Image Retrieval Using a Combined Graph and SOM Model

Hai,

Lang,

The Van

2023

IEEE Access

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Extracting similarity and semantic images from images is a hot topic that is used in various semantic retrieval systems. The GP-Tree, a hierarchical clustering tree, is used in the article to retrieve semantic images. The GP-Tree is then used to create a graph of neighboring leaf nodes, resulting in a smaller search list and a reduced chance of missing related similar images. In addition, the neighbor graph is used to build a Self-Organizing Map (SOM) to improve clustering efficiency and image retrieval performance. To improve high-level semantic retrieval efficacy, we also propose extending the current ontology structure. The ontology framework with rich domains represents most of the basic objects. With this ontology, the sets of images are added for the enrichment and can be used for many different sets of images. The SPARQL query is automatically generated from visual words for querying on the ontology. The query result on the ontology is a set of similar images and the definition of its semantics. The proposed method is tested on the WANG and ImageCLEF datasets, and the results are compared to previous publications on the same dataset, demonstrating its efficacy.INDEX TERMS Semantic-based image retrieval, Content-based image retrieval, Hierarchical clustering, Self-Organizing Map, Ontology.

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Pattern graph-based image retrieval system combining semantic and visual features

Cited by 7 publications

References 26 publications

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

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

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Improving the Efficiency of Semantic Image Retrieval Using a Combined Graph and SOM Model

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