scholarly journals AN OBJECT PROPERTIES FILTER FOR MULTI-MODALITY ONTOLOGY SEMANTIC IMAGE RETRIEVAL

2017 ◽  
Author(s):  
Mohd Suffian Sulaiman ◽  
Sharifalillah Nordin ◽  
Nursuriati Jamil
Keyword(s):  
Image Retrieval ◽  
Semantic Gap ◽  
Retrieval Algorithm ◽  
Semantic Image Retrieval ◽  
Knowledge Based ◽  
Object Properties ◽  
Resource Identification ◽  
Retrieval Mechanism ◽  
Main Components ◽  
High Level

Ontology is a semantic technology that provides the possible approach to bridge the issue on semantic gap in image retrieval between low-level visual features and high-level human semantic. The semantic gap occurs when there is a discrepancy between the information that is extracted from visual data and the text description. In other words, there is a difference between the computational representation in machine and human natural language. In this paper, an ontology has been utilized to reduce the semantic gap by developing a multi-modality ontology image retrieval with the enhancement of a retrieval mechanism by using the object properties filter. To achieve this, a multi-modality ontology semantic image framework was proposed, comprising of four main components which were resource identification, information extraction, knowledge-based construction and retrieval mechanism. A new approach, namely object properties filter is proposed by customizing the semantic image retrieval algorithm and the graphical user interface to facilitate the user to engage with the machine i.e. computers, in order to enhance the retrieval performance. The experiment results showed that the proposed approach delivered better results compared to the approach that did not use the object properties filter based on probability precision measurement.  

Research on High-Level Semantic Image Retrieval

2011 ◽  
Vol 268-270 ◽  
pp. 1427-1432
Author(s):  
Chang Yong Ri ◽  
Min Yao
Keyword(s):  
Image Retrieval ◽  
Image Understanding ◽  
Extraction Methods ◽  
Semantic Gap ◽  
Visual Features ◽  
Semantic Features ◽  
Image Description ◽  
Semantic Image Retrieval ◽  
Image Grammar ◽  
High Level

This paper presented the key problems to shorten “semantic gap” between low-level visual features and high-level semantic features to implement high-level semantic image retrieval. First, introduced ontology based semantic image description and semantic extraction methods based on machine learning. Then, illustrated image grammar on the high-level semantic image understanding and retrieval, and-or graph and context based methods of semantic image. Finally, we discussed the development directions and research emphases in this field.

scholarly journals An Enhanced Framework for Content Based Medical Image Retrieval using Deep Neural Network

2019 ◽  
Vol 8 (5S3) ◽  
pp. 239-244
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Low Cost ◽  
Image Features ◽  
Semantic Gap ◽  
Medical Image Retrieval ◽  
Large Size ◽  
Image Repository ◽  
High Level ◽  
Technology Growth

As the technology growth fuelled by low cost tech in the areas of compute, storage the need for faster retrieval and processing of data is becoming paramount for organizations. The medical domain predominantly for medical image processing with large size is critical for making life critical decisions. Healthcare community relies upon technologies for faster and accurate retrieval of images. Traditional, existing problem of efficient and similar medical image retrieval from huge image repository are reduced by Content Based Image Retrieval (CBIR) . The major challenging is an semantic gap in CBIR system among low and high level image features. This paper proposed, enhanced framework for content based medical image retrieval using DNN to overcome the semantic gap problem. It is outlines the steps which can be leveraged to search the historic medical image repository with the help of image features to retrieve closely relevant historic image for faster decision making from huge volume of database. The proposed system is assessed by inquisitive amount of images and the performance efficiency is calculated by precision and recall evaluation metrics. Experimental results obtained the retrieval accuracy is 79% based on precision and recall and this approach is preformed very effectively for image retrieval performance.

A Multi-Label Image Annotation With Multi-Level Tagging System

2018 ◽  
pp. 28-47
Author(s):  
Kalaivani Anbarasan ◽  
Chitrakala S.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Annotation ◽  
Image Features ◽  
Semantic Gap ◽  
Low Level ◽  
Image Retrieval System ◽  
Multi Level ◽  
High Level ◽  
Tagging System

The content based image retrieval system retrieves relevant images based on image features. The lack of performance in the content based image retrieval system is due to the semantic gap. Image annotation is a solution to bridge the semantic gap between low-level content features and high-level semantic concepts Image annotation is defined as tagging images with a single or multiple keywords based on low-level image features. The major issue in building an effective annotation framework is the integration of both low level visual features and high-level textual information into an annotation model. This chapter focus on new statistical-based image annotation model towards semantic based image retrieval system. A multi-label image annotation with multi-level tagging system is introduced to annotate image regions with class labels and extract color, location and topological tags of segmented image regions. The proposed method produced encouraging results and the experimental results outperformed state-of-the-art methods

Based on the Semantics of the Low-Level Visual Features Image Retrieval

2012 ◽  
Vol 482-484 ◽  
pp. 512-517
Author(s):  
Xian Wen Zeng ◽  
Xue Dong Shen
Keyword(s):  
Image Retrieval ◽  
Input Parameter ◽  
Paper Analysis ◽  
Image Features ◽  
Visual Features ◽  
Color Feature ◽  
Semantic Image Retrieval ◽  
Low Level ◽  
High Level

This paper analysis the reasons that traditional CBIR can’t support based Semantic image retrieval, and gave a kind of method that Using SVM may solute it. Through studying and Classification, combining HSV Color feature as input parameter ,it realized the connection and map between the high-level semantics and low-level image features .Using this method to retrieve can have proved to get higher accuracy.

Lifelog Image Retrieval Based on Semantic Relevance Mapping

2021 ◽  
Vol 17 (3) ◽  
pp. 1-18
Author(s):  
Qianli Xu ◽  
Ana Garcia Del Molino ◽  
Jie Lin ◽  
Fen Fang ◽  
Vigneshwaran Subbaraju ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Data Analytics ◽  
State Of The Art ◽  
Information Access ◽  
Research Area ◽  
Semantic Gap ◽  
Computational Framework ◽  
Semantic Relevance ◽  
High Level ◽  
Basic Semantic

Lifelog analytics is an emerging research area with technologies embracing the latest advances in machine learning, wearable computing, and data analytics. However, state-of-the-art technologies are still inadequate to distill voluminous multimodal lifelog data into high quality insights. In this article, we propose a novel semantic relevance mapping ( SRM ) method to tackle the problem of lifelog information access. We formulate lifelog image retrieval as a series of mapping processes where a semantic gap exists for relating basic semantic attributes with high-level query topics. The SRM serves both as a formalism to construct a trainable model to bridge the semantic gap and an algorithm to implement the training process on real-world lifelog data. Based on the SRM, we propose a computational framework of lifelog analytics to support various applications of lifelog information access, such as image retrieval, summarization, and insight visualization. Systematic evaluations are performed on three challenging benchmarking tasks to show the effectiveness of our method.

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