Special Issue "Image Based Information Retrieval from the Web"

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (30 August 2018)

Special Issue Editors

Guest Editor
Dr. Phivos Mylonas

Image, Video and Multimedia Systems Laboratory [IVML] Zografou Campus, Iroon Polytechneioy 9, PC 15773 ECE Building - 1st Floor - Room 11.23
Website | E-Mail
Interests: Knowledge-assisted multimedia analysis; Multimedia information retrieval; Multimedia personalization; User adaptation; User modeling; User profiling; Visual context representation and analysis; Human-computer interaction
Guest Editor
Prof. Dr. Evaggelos Spyrou

Department of Computer Engineering, Technological Educational Institute of Central Greece, P.C. 35100, Lamia, Greece
Website | E-Mail
Interests: computer vision; pattern recognition; semantic multimedia analysis; indexing and retrieval; multimedia content representation; biomedical image analysis

Special Issue Information

Dear Colleagues,

In recent years, following the tremendous growth of the Web, extremely large amounts of digital multimedia content are being produced every day and are shared online, mainly through several newly-emerged channels, such as social networks. Moreover, several digital content archives and datasets have become publicly available. Therefore, the field of image-based information retrieval has received a great deal of attention and on a wide range of topics dealing with every aspect of content handling. When designing and implementing an image-based retrieval system, and considering the continuous growth of digital content, one has to deal with several issues such as efficiency, accuracy, scaling, user-friendliness, and impact.

The intent of this Special Issue is to collect the experiences of leading scientists, but also to serve as an assessment tool for people who are new to the world of image-based information retrieval.

This Special Issue is intended to covering the following topics, but is not limited to them:

  • Feature extraction from visual content
  • Multimodal information fusion
  • 2D/3D detection, categorization and recognition
  • Scene recognition
  • Image and video retrieval, annotation, indexing
  • Datasets construction and analysis
  • Social media analysis, interaction and retrieval
  • Multimedia representation
  • Efficient large-scale image/video search
  • Deep learning techniques for detection and classification
  • Interfaces for querying, retrieval, exploration and visualization of multimedia databases
  • Applications and systems for image/video-based information retrieval
  • User-centric social multimedia computing
  • Personalized multimedia search
  • Semantic-based multimedia big data retrieval

Prof. Dr. Phivos Mylonas
Prof. Dr. Evaggelos Spyrou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Information retrieval
  • Multimedia content
  • Detection
  • Recognition
  • Classification
  • Fusion

Published Papers (5 papers)

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Research

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Open AccessArticle Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning
J. Imaging 2018, 4(11), 125; https://doi.org/10.3390/jimaging4110125
Received: 7 August 2018 / Revised: 2 October 2018 / Accepted: 18 October 2018 / Published: 23 October 2018
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Abstract
(1) Background: Evidence-based policymaking requires data about the local population’s socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View
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(1) Background: Evidence-based policymaking requires data about the local population’s socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however, these methods are also resource-intensive, since they require large volumes of manually labeled training data. (2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification, and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. (3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has R 2 = 0.76 and a correlation coefficient of 0.874 with the true unemployment rate, while it achieves a mean absolute percentage error of 0.089 and mean absolute error of 1.87 on a held-out test set. Similar results are also obtained for other socioeconomic indicators, related to education level and occupational prestige. (4) Conclusions: The proposed methodology can be used to estimate SES indicators at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort. Full article
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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Open AccessArticle An Ensemble SSL Algorithm for Efficient Chest X-Ray Image Classification
J. Imaging 2018, 4(7), 95; https://doi.org/10.3390/jimaging4070095
Received: 21 May 2018 / Revised: 3 July 2018 / Accepted: 13 July 2018 / Published: 20 July 2018
Cited by 2 | PDF Full-text (357 KB) | HTML Full-text | XML Full-text
Abstract
A critical component in the computer-aided medical diagnosis of digital chest X-rays is the automatic detection of lung abnormalities, since the effective identification at an initial stage constitutes a significant and crucial factor in patient’s treatment. The vigorous advances in computer and digital
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A critical component in the computer-aided medical diagnosis of digital chest X-rays is the automatic detection of lung abnormalities, since the effective identification at an initial stage constitutes a significant and crucial factor in patient’s treatment. The vigorous advances in computer and digital technologies have ultimately led to the development of large repositories of labeled and unlabeled images. Due to the effort and expense involved in labeling data, training datasets are of a limited size, while in contrast, electronic medical record systems contain a significant number of unlabeled images. Semi-supervised learning algorithms have become a hot topic of research as an alternative to traditional classification methods, exploiting the explicit classification information of labeled data with the knowledge hidden in the unlabeled data for building powerful and effective classifiers. In the present work, we evaluate the performance of an ensemble semi-supervised learning algorithm for the classification of chest X-rays of tuberculosis. The efficacy of the presented algorithm is demonstrated by several experiments and confirmed by the statistical nonparametric tests, illustrating that reliable and robust prediction models could be developed utilizing a few labeled and many unlabeled data. Full article
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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Open AccessArticle Digital Comics Image Indexing Based on Deep Learning
J. Imaging 2018, 4(7), 89; https://doi.org/10.3390/jimaging4070089
Received: 30 April 2018 / Revised: 21 June 2018 / Accepted: 27 June 2018 / Published: 2 July 2018
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Abstract
The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to
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The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed. Full article
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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Open AccessArticle Efficient Implementation of Gaussian and Laplacian Kernels for Feature Extraction from IP Fisheye Cameras
J. Imaging 2018, 4(6), 73; https://doi.org/10.3390/jimaging4060073
Received: 1 May 2018 / Revised: 15 May 2018 / Accepted: 17 May 2018 / Published: 24 May 2018
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Abstract
The Gaussian kernel, its partial derivatives and the Laplacian kernel, applied at different image scales, play a very important role in image processing and in feature extraction from images. Although they have been extensively studied in the case of images acquired by projective
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The Gaussian kernel, its partial derivatives and the Laplacian kernel, applied at different image scales, play a very important role in image processing and in feature extraction from images. Although they have been extensively studied in the case of images acquired by projective cameras, this is not the case for cameras with fisheye lenses. This type of cameras is becoming very popular, since it exhibits a Field of View of 180 degrees. The model of fisheye image formation differs substantially from the simple projective transformation, causing straight lines to be imaged as curves. Thus the traditional kernels used for processing images acquired by projective cameras, are not optimal for fisheye images. This work uses the calibration of the acquiring fisheye camera to define a geodesic metric for distance between pixels in fisheye images and subsequently redefines the Gaussian kernel, its partial derivatives, as well as the Laplacian kernel. Finally, algorithms for applying in the spatial domain these kernels, as well as the Harris corner detector, are proposed, using efficient computational implementations. Comparative results are shown, in terms of correctness of image processing, efficiency of application for multi scale processing, as well as salient point extraction. Thus we conclude that the proposed algorithms allow the efficient application of standard processing and analysis techniques of fisheye images, in the spatial domain, once the calibration of the specific camera is available. Full article
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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Other

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Open AccessTechnical Note DIRT: The Dacus Image Recognition Toolkit
J. Imaging 2018, 4(11), 129; https://doi.org/10.3390/jimaging4110129
Received: 25 August 2018 / Revised: 25 October 2018 / Accepted: 26 October 2018 / Published: 30 October 2018
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Abstract
Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which,
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Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be useful, must be transformed into actionable knowledge. Approaches based on Information Communication Technologies (ICT) have been shown to be able to help farmers and related stakeholders make decisions on problems by examining large volumes of data while assessing multiple criteria. In this paper, we address the automated identification (and count the instances) of the major threat of olive trees and their fruit, the Bactrocera Oleae (a.k.a. Dacus) based on images of the commonly used McPhail trap’s contents. Accordingly, we introduce the “Dacus Image Recognition Toolkit” (DIRT), a collection of publicly available data, programming code samples and web-services focused at supporting research aiming at the management the Dacus as well as extensive experimentation on the capability of the proposed dataset in identifying Dacuses using Deep Learning methods. Experimental results indicated performance accuracy (mAP) of 91.52% in identifying Dacuses in trap images featuring various pests. Moreover, the results also indicated a trade-off between image attributes affecting detail, file size and complexity of approaches and mAP performance that can be selectively used to better tackle the needs of each usage scenario. Full article
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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