New LIRE Demo

A new demo is up and running. It features ~ 1 million of images. Search is based on Solr with a custom plugin. See http://demo-itec.uni-klu.ac.at/liredemo

LIRE 0.9.5 released

It’s been more than a year since we made a release, so there have been lots of changes, fixes and new features. Most important of all is the integration of the SIMPLE descriptor, a local feature that works extremely well for content based image retrieval. This has been done with a lot of help from Nektarios Anagnostopoulos and Savvas Chatzichristofis!

Besides that we switched to OpenCV for SURF and SIFT extraction, added numerous bug fixes, updated Lucene to 4.10.2 and much more. Best you give it a try.

>> Head over to the downloads

MTAP Special Issue on Content Based Multimedia Indexing

Considering several requests, the submission deadline has been extended to November 30th.
In addition to the Content-Based Multimedia Indexing (CBMI) 2014 workshop, a special issue of Multimedia Tools and Applications is devoted to Content Based Multimedia Indexing. The issue is not restricted to papers accepted to the workshop, but all papers submitted should contain sufficient innovative material with respect to previously published work.
Multimedia indexing systems aim at providing easy, fast and accurate access to large multimedia repositories. Research in Content-Based Multimedia Indexing covers a wide spectrum of topics in content analysis, content description, content adaptation and content retrieval. Various tools and techniques from different fields such as Data Indexing, Machine Learning, Pattern Recognition, and Human Computer Interaction have contributed to the success of multimedia systems.
Although, there has been a significant progress in the field, we still face situations when the system shows limits in accuracy, generality and scalability. Hence, the goal of this special issue is to bring forward the recent advancements in content-based multimedia indexing. Submitted papers should contain significant original new information and ideas. Topics of interest for the Special Issue include, but are not limited to:
Visual Indexing
• Visual indexing (image, video, graphics)
• Visual content extraction
• Identification and tracking of semantic regions
• Identification of semantic events
Audio and Multi-modal Indexing
• Audio indexing (audio, speech, music)
• Audio content extraction
• Multi-modal and cross-modal indexing
• Metadata generation, coding and transformation
• Multimedia information retrieval
Multimedia retrieval (image, audio, video …)
• Matching and similarity search
• Content-based search
• Multimedia data mining
• Multimedia recommendation
• Large scale multimedia database management
Multimedia Browsing and Presentation
• Summarization, browsing and organization of multimedia content
• Personalization and content adaptation
• User interaction and relevance feedback
• Multimedia interfaces, presentation and visualization tools
Submission details
All the papers should be full journal length versions and follow the guidelines set out by Multimedia Tools and Applications: http://www.springer.com/journal/11042. Manuscripts should be submitted online at https://www.editorialmanager.com/mtap/choosing “CBMI 2014” as article type, no later than November 30th, 2014. When uploading your paper, please ensure that your manuscript is marked as being for this special issue. Information about the manuscript (title, full list of authors, corresponding author’s contact, abstract, and keywords) should also be sent to the corresponding editor Georges Quénot (Georges.Quenot@imag.fr). All the papers will be peer-reviewed following the MTAP reviewing procedures.
Important Dates (Tentative)
Submission of papers: November 30, 2014
Acceptance Notification: January 31, 2013
Submission of final manuscript: March 31, 2015
Publication of special issue: 2Q 2015
Guest Editors
Name: Dr. Georges Quénot Name: Dr. Harald Kosch
Email: Georges.Quenot@imag.f E-Mail: harald.kosch@uni-passau.de
Affiliation: CNRS-LIG Affiliation : University of Passau

The top 100 papers

Article from Nature

The discovery of high-temperature superconductors, the determination of DNA’s double-helix structure, the first observations that the expansion of the Universe is accelerating — all of these breakthroughs won Nobel prizes and international acclaim. Yet none of the papers that announced them comes anywhere close to ranking among the 100 most highly cited papers of all time.

Citations, in which one paper refers to earlier works, are the standard means by which authors acknowledge the source of their methods, ideas and findings, and are often used as a rough measure of a paper’s importance. Fifty years ago, Eugene Garfield published the Science Citation Index (SCI), the first systematic effort to track citations in the scientific literature. To mark the anniversary, Nature asked Thomson Reuters, which now owns the SCI, to list the 100 most highly cited papers of all time. (See the full list at Web of Science Top 100.xls or the interactive graphic, below.) The search covered all of Thomson Reuter’s Web of Science, an online version of the SCI that also includes databases covering the social sciences, arts and humanities, conference proceedings and some books. It lists papers published from 1900 to the present day.

Read the entire article

Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts

Big-data boondoggles and brain-inspired chips are just two of the things we’re really getting wrong

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool’s errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree.

Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report “Frontiers in Massive Data Analysis.” San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014.

Read the interview

http://spectrum.ieee.org/robotics/artificial-intelligence/machinelearning-maestro-michael-jordan-on-the-delusions-of-big-data-and-other-huge-engineering-efforts

How Smart Are Smartphones?: Bridging the marketing and information technology gap.

My latest IEEE article (co-authored with A. Amanatiadis) is out. Please read the article and send me your comments!!

How Smart Are Smartphones?: Bridging the marketing and information technology gap.

The term "smart" has become widespread in consumer electronics in recent years, reflecting the consumers' need for devices that assist them in their daily activities. The term has a long history of usage in marketing science as one of the most appealing ways of promoting or advertising a product, brand, or service. However, even today, there is much controversy in the definition of this term and even more ambiguities for the right use in consumer electronic devices. Furthermore, it is not possible to carry out any quantitative or qualitative analysis of how smart a device is without having some adequate conception of what a smart or intelligent application means. This article attempts to explore the smart and intelligent capabilities of the current and next-generation consumer devices by investigating certain propositions and arguments along with the current trends and future directions in information technology (IT).

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6914691&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6914657%29