Special Issue "Image Based Information Retrieval from the Web"

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

Deadline for manuscript submissions: 30 August 2018

Special Issue Editors

Guest Editor
Prof. Dr. Phivos Mylonas

Department of Informatics, Ionian University, P.C. 49100, Corfu, Greece
Website | E-Mail
Interests: knowledge management and acquisition; context representation and analysis; content-based information retrieval; knowledge-assisted multimedia analysis; multimedia personalization; user adaptation; user modeling and profiling
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 (3 papers)

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Research

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
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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
[...] Read more.
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
[...] Read more.
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
[...] Read more.
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Creating Playlist Thumbnails using Deep Learning
Authors: Ioannis Karydi, Spyros Sioutas, Markos Avlonitis
Affiliation: Department of Informatics, School of Information Sciences and Informatics, Ionian University, Greece
Abstract: Musical playlists have lately been one of the key widespread methods of musical content consumption, in contrast to selecting individual tracks. Numerous musical content providers and Music Information Research works utilize a wealth of metadata-, content- as well as context- based methodologies in order to provide customized and event-oriented such playlists. The abundance of playlists as well as the difficulty of fast preview of playlists' content, due to their temporal character, has thus lead to the requirement of creating a form of aggregated presentation for their selection among a litany of alternatives. Accordingly, in this work we propose a Deep Learning algorithm that allows the creation of novel musical artwork in the form of images for a playlist based on its tracks' content, album artwork as well as contextual information assigned by users on social media.

Title: A Deep Learning Method for Automated Bactrocera Oleae Identification from Images of McPhail Traps' Contents
Authors: Romanos Kalamatianos1, Ioannis Karydis1, Markos Avlonitis1, Pavlos Bouchagier2
1
Department of Informatics, Ionian University, Greece
2 Technological Educational Institute of Ionian Islands, Greece
Abstract: Modern agriculture is facing unique challenges in building a sustainable future in a way that empowers the agricultural sector to meet the world’s food needs. Reliable detection of plantation threats by pests/diseases as well as proper quantification of induced damages are thus critical. As olive trees are the most dominant permanent crop within EU in terms of occupied areas, our work focuses on one of the major threats, the olive-fruit fly. Measurements of the fly's infestation in olive groves are predominantly done with manual methods involving traps, while the key requirement in verifying an outbreak lies in measuring the pests collected in the trap over a time-span. This process necessitates frequent and time consuming manual checks while no other parameter of the trap does so. Advanced traps, feature a camera taking pictures of the trap's contents that are then sent over a network to interested parties. Accordingly, in this work we propose a Deep Learning Method for automated identification of the olive fruit fly based on the aforementioned images of McPhail traps' contents.

Topic: The Development of A Methodology for Extracting Characterizations/Categorizations of The Urban Environment Using Sources Like Google Maps And Street View
Authors: Anastasios Delopoulos and Christos Diou
Affiliation: School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece

Title: Recent Trends And Case Studies in Image Indexing and Retrieval
Authors: Sule Yildirim Yayilgan, Claudia Companioni Brito, Zygred Calibjo, Lissete Paiz, Nadile Nudes and LAM Yat Hong
Affiliation: Norwegian University of Science and Technology, Norway

Topic: Photogrammetric Reconstruction Using Photos Mined from Large Internet Collections
Authors: Stamatis Chatzistamatis1, Christos-Nikolaos Anagnostopoulos1, George E. Tsekouras1, Dimitrios Makris2
1 Department of Cultural Technology and Communication, University of the Aegean, Greece
2 Department of Computer Science, Kingston University London, UK

Author: Mahmudur Rahman
Affiliation: Department of Computer Science, Morgan State University, USA

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