12th International Content Based Multimedia Indexing Workshop

Following the eleven successful previous events of CBMI (Toulouse 1999, Brescia 2001, Rennes 2003, Riga 2005, Bordeaux 2007, London 2008, Chania 2009, Grenoble 2010, Madrid 2011, Annecy 2012, and Veszprem 2013), the 12th International Content Based Multimedia Indexing Workshop aims to bring together communities involved in all aspects of content-based multimedia indexing, retrieval, browsing and presentation. CBMI 2014 will take place in Klagenfurt, in the very south of Austria from June 18th to June 20th 2014. CBMI 2014 is organized in cooperation with IEEE Circuits and Systems Society and ACM SIG Multimedia. Topics of the workshop include but are not limited to visual indexing, audio and multi-modal indexing, multimedia information retrieval, and multimedia browsing and presentation. Additional special sessions are planned in  the fields of endoscopic videos and images and multimedia metadata.

Topics:

Topics of interest, grouped in technical tracks, 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

Paper submission

Authors are invited to submit full-length and special session papers of 6 pages and short (poster) and demo papers of 4 pages maximum. All peer-reviewed, accepted and registered papers will be published in the CBMI 2014 workshop proceedings  to be indexed and distributed by the IEEE Xplore. The submissions are peer reviewed in single blind process, the language of the workshop is English.

Best papers of the conference will be invited to submit extended versions of their contributions to a special issue ofMultimedia Tools and Applications journal (MTAP).

Important dates:

• Paper submission deadline: February 16, 2014
• Notification of acceptance: March 30, 2014
• Camera-ready papers due: April 14, 2014
• Author registration: April 14, 2014
• Early registration: May 25, 2014

Contact

For more information please visit http://cbmi2014.itec.aau.at/ and for additional questions, remarks, or clarifications please contact cbmi2014@itec.aau.at

A Futuristic Short Film HD: by Sight Systems

Mobile Robotics: Mathematics, Models, and Methods

New book

Mobile Robotics offers comprehensive coverage of the essentials of the field suitable for both students and practitioners. Adapted from Alonzo Kelly's graduate and undergraduate courses, the content of the book reflects current approaches to developing effective mobile robots. Professor Kelly adapts principles and techniques from the fields of mathematics, physics, and numerical methods to present a consistent framework in a notation that facilitates learning and highlights relationships between topics. This text was developed specifically to be accessible to senior level undergraduates in engineering and computer science, and includes supporting exercises to reinforce the lessons of each section. Practitioners will value Kelly's perspectives on practical applications of these principles. Complex subjects are reduced to implementable algorithms extracted from real systems wherever possible, to enhance the real-world relevance of the text.

 

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Why Does Google Need So Many Robots?

To Jump From The Web To The Real World

Original Article: techcrunch

Why does Google need robots? Because it already rules your pocket. The mobile market, except for the slow rise of wearables, is saturated. There are millions of handsets around the world, each one connected to the Internet and most are running either Android or iOS. Except for incremental updates to the form, there will be few innovations coming out of the mobile space in the next decade.

Then there’s Glass. These devices bring the web to the real world by making us the carriers. Google is already in front of us on our small screens but Glass makes us a captive audience. By depending on Google’s data for our daily interactions, mapping, and restaurant recommendations – not to mention the digitization of our every move – we become some of the best Google consumers in history. But that’s still not enough.

Google is limited by, for lack of a better word, meat. We are poor explorers and poor data gatherers. We tend to follow the same paths every day and, like ants, we rarely stray far from the nest. Google is a data company and needs far more data than humans alone can gather. Robots, then will be the driver for a number of impressive feats in the next few decades including space exploration, improved mapping techniques, and massive changes in the manufacturing workspace.

Robots like Baxter will replace millions of expensive humans – a move that I suspect will instigate a problematic rise of unemployment in the manufacturing sector – and companies like manufacturing giant Foxconn are investing in robotics at a clip. Drones, whether human-control or autonomous, are a true extension of our senses, placing us and keeping us apprised of situations far from home base. Home helpers will soon lift us out of bed when we’re sick, help us clean, and assist us near the end of our lives. Smaller hardware projects will help us lose weight and patrol our streets. The tech company not invested in robotics today will find itself far behind the curve in the coming decade.

That’s why Google needs robots. They will place the company at the forefront of man-machine interaction in the same way that Android put them in front of millions of eyeballs. Many pundits saw no reason for Google to start a mobile arm back when Android was still young. They were wrong. The same will be the case for these seemingly wonky experiments in robotics.

Did Google buy Boston Dynamics and seven other robotics companies so it could run a thousand quadrupedal Big Dogs through our cities? No, but I could see them using BD’s PETMAN, a bipedal robot that can walk and run over rough terrain – to assist in mapping difficult-to-reach areas. It could also become a sort of Google Now for the real world, appearing at our elbows in the form of an assistant that follows us throughout the day, keeping us on track, helping with tasks, and becoming our avatars when we can’t be in two places at once. The more Google can mediate our day-to-day experience the more valuable it becomes.

Need more proof? [Read More]

Google buys Boston Dynamics, maker of spectacular and terrifying robots

Google has acquired robotics engineering company Boston Dynamics, best known for its line of quadrupeds with funny gaits and often mind-blowing capabilities. Products that the firm has demonstrated in recent years include BigDog, a motorized robot that can handle ice and snow, the 29 mile-per-hour Cheetah, and an eerily convincing humanoid known as PETMAN. News of the deal was reported on Friday by The New York Times, which says that the Massachusetts-based company's role in future Google projects is currently unclear.

Specific details about the price and terms of the deal are currently unknown, though Google told the NYT that existing contracts — including a $10.8 million contract inked earlier this year with the US Defense Agency Research Projects Agency (DARPA) — would be honored. Despite the DARPA deal, Google says it doesn't plan to become a military contractor "on its own," according to the Times.

Boston Dynamics began as a spinoff from the Massachusetts Institute of Technology in 1992, and quickly started working on projects for the military. Besides BigDog, that includes Cheetah, an animal-like robot developed to run at high speeds, which was followed up by a more versatile model called WildCat. It's also worked on Atlas, a humanoid robot designed to work outdoors.

In a tweet, Google's Andy Rubin — who formerly ran Google's Android division — said the "future is looking awesome."

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My plan for a content based image search

Original Article

I saw this job posting from EyeEm, a photo sharing app / service, in which they express their wish/plan to build a search engine that can ‘identify and understand beautiful photographs’. That got me thinking about how I would approach building a system like that.

Here is how I would start:

1. Define what you are looking for

EyeEm already has a search engine based on tags and geo-location. So I assume, they want to prevent low quality pictures to appear in the results and add missing tags to pictures, based on the image’s content. One could also group similar looking pictures or rank those pictures lower which “don’t contain their tags”.  For instance for the Brandenburger Torthere are a lot of similar looking pictures and even some that don’t contain the gate at all.

But for which concepts should one train the algo-rithms? Modern image retrieval systems are trained for hundreds of concepts, but I don’t think it is wise to start with that many. Even the most sophisticated, fine tuned systems have high error rates for most of the concepts as can be seen in this year’s results of the Large Scale Visual Recognition Challenge.

For instance the team from EUVision / University of Amsterdam, placed 6 in the classification challenge, only selected 16 categories for their consumer app Impala. For a consumer application I think their tags are a good choice:

• Architecture
• Babies
• Beaches
• Cars
• Cats (sorry, no dogs)
• Children
• Food
• Friends

• Indoor
• Men
• Mountains
• Outdoor
• Party life
• Sunsets and sunrises
• Text
• Women

But of course EyeEm has the luxury of looking at their log files to find out what their users are actually searching for.

And on a comparable task of classifying pictures into 15 scene categories a team from MIT under Antonio Torralba showed that even with established algorithms one can achieve nearly 90% accuracy [Xiao10]. So I think it’s a good idea to start with a limited number of standard and EyeEm specific concepts, which allows for usable recognition accuracy even with less sophisticated approaches.

But what about identifying beautiful photographs? I think in image retrieval there is no other concept which is more desirable and challenging to master. What does beautiful actually mean? What features make a picture beautiful? How do you quantify these features? Is beautiful even a sensibly concept for image retrieval? Might it be more useful trying to predict which pictures will be `liked` or `hearted` a lot? These questions have to be answered before one can even start experimenting. I think for now it is wise to start with just filtering out low quality pictures and to try to predict what factors make a picture popular.

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