Semantic extraction for images is an urgent problem and is applied in many different semantic retrieval systems. In this paper, a semantic-based image retrieval (SBIR) system is proposed based on the combination of growth partitioning tree (GP-Tree), which was built in the authors’ previous work, with a self-organizing map (SOM) network and neighbor graph (called SgGP-Tree) to improve accuracy. For each query image, a similar set of images is retrieved on the SgGP-Tree, and a set of visual words is extracted relying on the classes obtained from mask region-based convolutional neural networks (R-CNN), as the basis for querying semantic of input images on ontology by simple protocol and resource description framework query language (SPARQL) query. The experiment was performed on image datasets, such as ImageCLEF and MS-COCO, with precision values of 0.898453 and 0.875467, respectively. These results are compared with related works on the same image dataset, showing the effectiveness of the methods proposed. 

A method for semantic-based image retrieval using hierarchical clustering tree and graph

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.

Improving The Efficiency of Semantic Image Retrieval using A Combined Graph and SOM Model

Semantic extraction for images is a topical problem and is applied in many different semantic search systems. In this paper, a method of semantic image retrieval is proposed based on the set of similar images to the input image; then, the semantics of the images are queried on the ontology through the visual words vector. The objects of each image are extracted and classified by the Mask R-CNN and stored on the cluster graph to extract semantics for the image. The similar images of query image are extracted on the cluster graph; then, the k-NN algorithm is applied to find the visual words vector as the basis for querying the semantic of the query image on the ontology by the SPARQL query. On the basis of the proposed method, an experiment was built and evaluated on two large-volume image datasets MIRFLICKR-25K and MS COCO. Experimental results are compared with recently published works on the same datasets to demonstrate the effectiveness of the proposed method. According to the experimental results, the method of semantic image retrieval in this paper has improved the accuracy to 0.897 for MIRFLICKR-25K, 0.833 for MS COCO.

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A method of semantic-based image retrieval using graph cut

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A method of semantic-based image retrieval using graph cut