Semantic based Image Retrieval
TL;DR: Semantic Image Retrieval is based on hybrid approach and uses shape, color and texture based approaches for classification purpose and tries to map the low level image features to high level ontology concepts.
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Abstract: Images / Videos are major source of content on the internet and the content is increasing rapidly due to the advancement in this area. Image analysis and retrieval is one of the active research field and researchers from the last decade have proposed many efficient approaches for the same. Semantic technologies offers promising approach to image retrieval as it tries to map the low level image features to high level ontology concepts. In this paper, we have proposed various Semantic Image Retrieval method for image retrieval relevant to the user query. Semantic Image Retrieval is based on hybrid approach and uses shape, color and texture based approaches for classification purpose.
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Citations
OntoKnowNHS: Ontology Driven Knowledge Centric Novel Hybridised Semantic Scheme for Image Recommendation Using Knowledge Graph.
N. Roopak,Gerard Deepak +1 more
- 22 Nov 2021
TL;DR: In this paper, the authors proposed OntoKnowNHS model which is composed of domain ontology based query term enrichment and knowledge enrichment of the images with the help of Google Knowledge Base API and Wikidata, incorporated with the knowledge graph of images which is compared using Convolutional Neural Networks and, the semantic similarity is computed using Kullback Leibler Divergence, Concept Similarity, and Normalised Compression Distance which recommends images from both Knowledge Graphs and Redefined Image Tag set.
22
Multi-Feature Fusion for Crime Scene Investigation Image Retrieval
Ying Liu,Dan Hu,Jiulun Fan,Fuping Wang,Dengsheng Zhang +4 more
- 01 Nov 2017
TL;DR: A DCT domain texture feature extraction algorithm is proposed for CSI images, which is shown to be simple and effective and Experimental results prove that the proposed method is effective for CSI image retrieval.
17
Multi-feature fusion with SVM classification for crime scene investigation image retrieval
Ying Liu,Fuping Wang,Dan Hu,Jiulun Fan +3 more
- 01 Aug 2017
TL;DR: Experimental results on real CSI image data show that the fusion feature proposed in this paper can well describe the content of CSI images, with an average 15.3% increment in retrieval precision compared with all the single-feature-based algorithm.
7
A method for semantic-based image retrieval using hierarchical clustering tree and graph
01 Oct 2022
TL;DR: In this article , 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 to improve accuracy.
2
A method of semantic-based image retrieval using graph cut
TL;DR: 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 to extract semantics for the image.
References
Content-based image retrieval at the end of the early years
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Semantic Image Retrieval: An Ontology Based Approach
TL;DR: This paper has proposed Semantic Image Retrieval: An Ontology based Approach which uses domain specific ontology for image retrieval relevant to the user query and shows the efficiency / accuracy of the proposed system and support the implementation of the same.
•Journal Article
A sketch based image retrieval: a review of literature
TL;DR: This survey paper reviews the development of Content Based Image Retrieval field and especially the sketch based image retrieval (SBIR) as a core issue and presents views in image retrieval based on sketch query, which is also the future direction.
Semantics-sensitive Retrieval for Digital Picture Libraries
TL;DR: SIMPLIcity (Semantics-sensitive Integrated Matching for Picture LIbraries), an image database retrieval system, which uses high-level semantics classification and integrated region matching based upon image segmentation to enhance retrieval by narrowing down the searching range in a database and permitting semantically adaptive searching methods.
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