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.

scholarly journals Combined global and local semantic feature–based image retrieval analysis with interactive feedback

2019 ◽  
Vol 53 (1-2) ◽  
pp. 3-17
Author(s):  
A Anandh ◽  
K Mala ◽  
R Suresh Babu
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Research Work ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Retrieval System ◽  
False Image ◽  
Active Research ◽  
High Level ◽  
Interactive Feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

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.

scholarly journals Composing High-level Stream Processing Pipelines

2020 ◽  
Author(s):  
Tanmaya Mahapatra
Keyword(s):  
Data Analytics ◽  
State Of The Art ◽  
Stream Processing ◽  
Detailed Comparison ◽  
Application Development ◽  
Actor Model ◽  
Data Transformations ◽  
Simulation Scenario ◽  
Iot Devices ◽  
High Level

Abstract The growing number of Internet of Things (IoT) devices provide a massive pool of sensing data. However, turning data into actionable insights is not a trivial task, especially in the context of IoT, where application development itself is complex. The process entails working with heterogeneous devices via various communication protocols to co-ordinate and fetch datasets, followed by a series of data transformations. Graphical mashup tools, based on the principles of flow-based programming paradigm, operating at a higher-level of abstraction are in widespread use to support rapid prototyping of IoT applications. Nevertheless, the current state-of-the-art mashup tools suffer from several architectural limitations which prevent composing in-flow data analytics pipelines. In response to this, the paper contributes by (i) designing novel flow-based programming concepts based on the actor model to support data analytics pipelines in mashup tools, prototyping the ideas in a new mashup tool called aFlux and providing a detailed comparison with the existing state-of-the-art and (ii) enabling easy prototyping of streaming applications in mashup tools by abstracting the behavioural configurations of stream processing via graphical flows and validating the ease as well as the effectiveness of composing stream processing pipelines from an end-user perspective in a traffic simulation scenario.

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

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 Insight on Electronic Travel Aids for Visually Impaired People: A Review on the Electromagnetic Technology

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1281 ◽  
Author(s):  
Emanuele Cardillo ◽  
Alina Caddemi
Keyword(s):  
Visually Impaired ◽  
State Of The Art ◽  
Research Area ◽  
Emerging Technology ◽  
Blind People ◽  
Comprehensive Description ◽  
Visually Impaired People ◽  
Impaired People ◽  
High Level ◽  
Electronic Travel Aids

This review deals with a comprehensive description of the available electromagnetic travel aids for visually impaired and blind people. This challenging task is considered as an outstanding research area due to the rapid growth in the number of people with visual impairments. For decades, different technologies have been employed for solving the crucial challenge of improving the mobility of visually impaired people, but a suitable solution has not yet been developed. Focusing this contribution on the electromagnetic technology, the state-of-the-art of available solutions is demonstrated. Electronic travel aids based on electromagnetic technology have been identified as an emerging technology due to their high level of achievable performance in terms of accuracy, flexibility, lightness, and cost-effectiveness.

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.  

scholarly journals Gated Fully Fusion for Semantic Segmentation

2020 ◽  
Vol 34 (07) ◽  
pp. 11418-11425 ◽  
Author(s):  
Xiangtai Li ◽  
Houlong Zhao ◽  
Lei Han ◽  
Yunhai Tong ◽  
Shaohua Tan ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Semantic Gap ◽  
Comprehensive Understanding ◽  
Deep Convolutional Neural Networks ◽  
Multi Level ◽  
Multiple Levels ◽  
High Level ◽  
Fully Connected

Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior results for small/thin objects where detailed information is important. It is natural to consider importing low level features to compensate for the lost detailed information in high-level features. Unfortunately, simply combining multi-level features suffers from the semantic gap among them. In this paper, we propose a new architecture, named Gated Fully Fusion(GFF), to selectively fuse features from multiple levels using gates in a fully connected way. Specifically, features at each level are enhanced by higher-level features with stronger semantics and lower-level features with more details, and gates are used to control the propagation of useful information which significantly reduces the noises during fusion. We achieve the state of the art results on four challenging scene parsing datasets including Cityscapes, Pascal Context, COCO-stuff and ADE20K.

scholarly journals Composing high-level stream processing pipelines

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tanmaya Mahapatra
Keyword(s):  
Data Analytics ◽  
State Of The Art ◽  
Stream Processing ◽  
Detailed Comparison ◽  
Application Development ◽  
Actor Model ◽  
Data Transformations ◽  
Simulation Scenario ◽  
Iot Devices ◽  
High Level

Abstract The growing number of Internet of Things (IoT) devices provide a massive pool of sensing data. However, turning data into actionable insights is not a trivial task, especially in the context of IoT, where application development itself is complex. The process entails working with heterogeneous devices via various communication protocols to co-ordinate and fetch datasets, followed by a series of data transformations. Graphical mashup tools, based on the principles of flow-based programming paradigm, operating at a higher-level of abstraction are in widespread use to support rapid prototyping of IoT applications. Nevertheless, the current state-of-the-art mashup tools suffer from several architectural limitations which prevent composing in-flow data analytics pipelines. In response to this, the paper contributes by (i) designing novel flow-based programming concepts based on the actor model to support data analytics pipelines in mashup tools, prototyping the ideas in a new mashup tool called aFlux and providing a detailed comparison with the existing state-of-the-art and (ii) enabling easy prototyping of streaming applications in mashup tools by abstracting the behavioural configurations of stream processing via graphical flows and validating the ease as well as the effectiveness of composing stream processing pipelines from an end-user perspective in a traffic simulation scenario.

scholarly journals Composing High-level Stream Processing Pipelines

2020 ◽  
Author(s):  
Tanmaya Mahapatra
Keyword(s):  
Data Analytics ◽  
State Of The Art ◽  
Stream Processing ◽  
Detailed Comparison ◽  
Application Development ◽  
Actor Model ◽  
Data Transformations ◽  
Simulation Scenario ◽  
Iot Devices ◽  
High Level

Abstract The growing number of Internet of Things (IoT) devices provide a massive pool of sensing data. However, turning data into actionable insights is not a trivial task, especially in the context of IoT, where application development itself is complex. The process entails working with heterogeneous devices via various communication protocols to co-ordinate and fetch datasets, followed by a series of data transformations. Graphical mashup tools, based on the principles of flow-based programming paradigm, operating at a higher-level of abstraction are in widespread use to support rapid prototyping of IoT applications. Nevertheless, the current state-of-the-art mashup tools suffer from several architectural limitations which prevent composing in-flow data analytics pipelines. In response to this, the paper contributes by (i) designing novel flow-based programming concepts based on the actor model to support data analytics pipelines in mashup tools, prototyping the ideas in a new mashup tool called aFlux and providing a detailed comparison with the existing state-of-the-art and (ii) enabling easy prototyping of streaming applications in mashup tools by abstracting the behavioural configurations of stream processing via graphical flows and validating the ease as well as the effectiveness of composing stream processing pipelines from an end-user perspective in a traffic simulation scenario.

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