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
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
Prof. Dr. Evaggelos Spyrou
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
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.
- Information retrieval
- Multimedia content
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