• Journal of Advanced Marine Engineering and Technology
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• v.33 no.4
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• pp.569-577
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• 2009

Mobile information retrieval is an evolving branch of information retrieval that is centered on mobile and ubiquitous environments. In general, mobile devices are characterized by lightweight, low power, small memory, small display, limited input/output, low bandwidth, and so on. Some of these characteristics make it impossible to apply general information retrieval to mobile environments without any modification. In order to relieve this problem, we design and implement an information retrieval system for mobile devices like wireless phones, PDA and handheld devices. We use document summarization techniques to alleviate the limitation of small display and user profiles to retrieve the most proper documents for each individual user for personalized search. Futhermore we use meta-search to lighten some burdens visiting several portal sites. In this paper, we have implemented and demonstrated the proposed mobile information retrieval system on the domain of travel and received good evaluation from users subjectively.

• International Journal of Contents
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• v.8 no.2
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• pp.1-6
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• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1498-1504
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• 2016

The progress of mobile computing technology is bringing a large amount of multimedia contents such as image. Thus, we need an image retrieval system which searches semantically relevant image. In this paper, we propose a semantic image annotation and retrieval in mobile environments. Previous mobile-based annotation approaches cannot fully express the semantics of image due to the limitation of current form (i.e., keyword tagging). Our approach allows mobile devices to annotate the image automatically using the context-aware information such as temporal and spatial data. In addition, since we annotate the image using RDF(Resource Description Framework) model, we are able to query SPARQL for semantic image retrieval. Our system implemented in android environment shows that it can more fully represent the semantics of image and retrieve the images semantically comparing with other image annotation systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4856-4872
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• 2018

The mobile internet has grown ubiquitous across the globe with the widespread use of smart devices. However, the designs of modern mobile operating systems and their applications limit content retrieval with mobile applications. The mobile internet is not as accessible as the traditional web, having more man-made restrictions and lacking a unified approach for crawling and content retrieval. In this study, we propose an automatic and scalable mobile application content crawler, which can recognize the interaction paths of mobile applications, representing them as interaction graphs and automatically collecting content according to the graphs in a parallel manner. The crawler was verified by retrieving content from 50 non-game applications from the Google Play Store using the Android platform. The experiment showed the efficiency and scalability potential of our crawler for large-scale mobile internet content retrieval.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.36-41
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• 2021

Text-based image retrieval is not only cumbersome as it requires the manual input of keywords by the user, but is also limited in the semantic approach of keywords. However, content-based image retrieval enables visual processing by a computer to solve the problems of text retrieval more fundamentally. Vision applications such as extraction and mapping of image characteristics, require the processing of a large amount of data in a mobile environment, rendering efficient power consumption difficult. Hence, an effective image retrieval method on mobile platforms is proposed herein. To provide the visual meaning of keywords to be inserted into images, the efficiency of image retrieval is improved by extracting keywords of exchangeable image file format metadata from images retrieved through a content-based similar image retrieval method and then adding automatic keywords to images captured on mobile devices. Additionally, users can manually add or modify keywords to the image metadata.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.30-41
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• 2002

We focus on the mobile agents which are considered as new paradigm to solve information retrieval of large volumes of data in the distributed and heterogeneous environment. The mobile agent moves the computation to data instead of large volumes of data to computations. In this paper, we propose an information retrieval model, which can effectively search data in the distributed and heterogeneous environment, using mobile agents. Our model is applied to the design and implementation of an Q&A(Question and Answer) retrieval system. Our Q&A retrieval system, called QASSMA(Q&A Search System using Mobile Agents), uses mobile agents to retrieve articles from Q&A boards and newsgroups that exist in the heterogeneous and distributed environment. QASSMA has the following features and advantages. First, the mobile retrieval agent moves to the destination server to retrieve articles to reduce the retrieval time by eliminating data traffics from the server to the client host. Also it can reduce the traffic that was occurred in the centralized network system, and reduce the usage of resources by sending its agent and running in the destination host. Finally, the mobile retrieval agent of QASSMA can add and update dynamically the class file according to its retrieval environment, and support other retrieval manner. In this paper, we have shown that our Q&A retrieval system using mobile agents is more efficient than the retrieval system using static agents by our experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.450-470
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• 2023

Traditional cloud computing faces some challenges such as huge energy consumption, network delay and single point of failure. Edge computing is a typical distributed processing platform which includes multiple edge servers closer to the users, thus is more robust and can provide real-time computing services. Although outsourcing data to edge servers can bring great convenience, it also brings serious security threats. In order to provide image retrieval while ensuring users' data privacy, a privacy preserving image retrieval scheme in edge environment is proposed. Considering the distributed characteristics of edge computing environment and the requirement for lightweight computing, we present a privacy-preserving image retrieval scheme in edge computing environment, which two or more "honest but curious" servers retrieve the image quickly and accurately without divulging the image content. Compared with other traditional schemes, the scheme consumes less computing resources and has higher computing efficiency, which is more suitable for resource-constrained edge computing environment. Experimental results show the algorithm has high security, retrieval accuracy and efficiency.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.56-63
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• 2024

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.305-323
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• 2010

This paper proposes an efficient and yet powerful local descriptor called phase-space partition based descriptor (PPD). This descriptor is designed for the mobile image matching and retrieval. PPD, which is inspired from SIFT, also encodes the salient aspects of the image gradient in the neighborhood around an interest point. However, without employing SIFT's smoothed gradient orientation histogram, we apply the region based gradient statistics in phase space to the construction of a feature representation, which allows to reduce much computation requirements. The feature matching experiments demonstrate that PPD achieves favorable performance close to that of SIFT and faster building and matching. We also present results showing that the use of PPD descriptors in a mobile image retrieval application results in a comparable performance to SIFT.

• International Journal of Knowledge Content Development & Technology
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• v.8 no.1
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• pp.37-50
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• 2018

The purpose of the study is to identify the perception of the Library and Information Science (LIS) specialists about the use of mobile phones in retrieving information. It investigates the present status of the LIS specialists regarding mobile phone usage, perceptions regarding the use of mobile phones in retrieving information from libraries and points out the possible drawbacks and their solutions for embracing mobile phones in retrieving information from libraries and providing some suggestions. The methodology includes a quantitative approach. The authors used a survey method using a structured questionnaire to measure the perception of LIS specialists regarding the use of mobile phones in retrieving information from libraries. The findings confirmed that the majority (72%) of LIS specialists have the experience of using mobile phones for more than 10 years. Findings also indicate that the LIS specialists have agreed with the statements of advantages of using mobile phones. This paper concludes that the respondents also agreed regarding the possible advantages of introducing mobile phones in retrieving information from libraries. The paper explores the original perceptions of LIS specialists regarding the use of mobile phones in retrieving information from libraries.