Evaluation of Image Browsing Interfaces for Smartphones and Tablets

Abstract

In this work we propose an early prototype of a video browser for mobile devices with touchscreens. We concentrate on utilizing the  thumbs because of the natural posture used with the devices when watching videos in landscape mode. The controls are only displayed when the user touches the screen and automatically rearrange themselves depending on the position of the thumbs. A combination of a radial menu and an extended seeker control with hierarchical browsing and bookmarking features enables the user to navigate quickly through videos.

2013 IEEE International Symposium on Multimedia

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Xirong Li receives SIGMM Best Ph.D. Thesis Award 2013

Article from http://www.ceessnoek.info/

Congratulations to dr. Xirong Li for receiving the SIGMM Award for Outstanding PhD Thesis in Multimedia Computing, Communications and Applications 2013. The committee considered Xirong’s dissertation titled “Content-based visual search learned from social media” as worthy of the award as it substantially extends the boundaries for developing content-based multimedia indexing and retrieval solutions. In particular, it provides fresh new insights into the possibilities for realizing image retrieval solutions in the presence of vast information that can be drawn from the social media.

The committee considered the main innovation of Xirong’s work to be in the development of the theory and algorithms providing answers to the following challenging research questions:
(a) what determines the relevance of a social tag with respect to an image,
(b) how to fuse tag relevance estimators,
(c) which social images are the informative negative examples for concept learning,
(d) how to exploit socially tagged images for visual search and
(e) how to personalize automatic image tagging with respect to a user’s preferences.

The significance of the developed theory and algorithms lies in their power to enable effective and efficient deployment of the information collected from the social media to enhance the datasets that can be used to learn automatic image indexing mechanisms (visual concept detection) and to make this learning more personalized for the user.

Xirong’s thesis is available from the UvA digital academic repository.

Post-Doc position on breast cancer image analysis (Computer Vision)

Location: Spain

Employer: Rovira i Virgili University

The Intelligent Robotics and Computer Vision group at the University Rovira i Virgili, Tarragona, Catalonia (Spain) is looking for candidates to work in a project related to breast cancer image analysis.

The Post-Doc positions are funded by the Government of Catalonia under the next program:

http://www10.gencat.cat/agaur_web/AppJava/english/a_beca.jsp?categoria=postdoctorals&id_beca=19944

Prospective candidates must have obtained a PhD degree on Computer Science (or Computer Engineering) between the 01/01/2007 and 31/12/2011.

Interested candidates please contact to Dr. Domenec Puig domenec.puig@urv.cat

ICME libdash Demo

This is the official MPEG-DASH Reference Software.

Top notch AI system about as smart as a four-year-old, lacks commonsense

Article from http://www.gizmag.com/ai-system-iq-four-year-old/28321/

Those who saw IBM’s Watson defeat former winners on Jeopardy! in 2011 might be forgiven for thinking that artificially intelligent computer systems are a lot brighter than they are. While Watson was able to cope with the highly stylized questions posed during the quiz, AI systems are still left wanting when it comes to commonsense. This was one of the factors that led researchers to find that one of the best available AI systems has the average IQ of a four-year-old.

To see just how intelligent AI systems are, a team of artificial and natural knowledge researchers at the University of Illinois as Chicago (UIC) subjected ConceptNet 4 to the verbal portions of the Weschsler Preschool and Primary Scale of Intelligence Test, which is a standard IQ test for young children. ConceptNet 4 is an AI system developed at MIT that relies on a commonsense knowledge base created from facts contributed by thousands of people across the Web.

While the UIC researchers found that ConceptNet 4 is on average about as smart as a four-year-old child, the system performed much better at some portions of the test than others. While it did well on vocabulary and in recognizing similarities, its overall score was brought down dramatically by a bad result in comprehension, or commonsense “why” questions.

“If a child had scores that varied this much, it might be a symptom that something was wrong,” said Robert Sloan, professor and head of computer science at UIC, and lead author on the study. “We’re still very far from programs with commonsense–AI that can answer comprehension questions with the skill of a child of eight.”

Sloan says AI systems struggle with commonsense because it relies not only on a large collection of facts, which computers can access easily through a database, but also on obvious things that we don’t even know we know – things that Sloan calls “implicit facts.” For example, a computer may know that water freezes at 32° F (0° C), but it won’t necessarily know that it is cold, which is something that even a four-year-old child will know.

“All of us know a huge number of things,” says Sloan. “As babies, we crawled around and yanked on things and learned that things fall. We yanked on other things and learned that dogs and cats don’t appreciate having their tails pulled.”

Sloan and his colleagues hope their study will hope identify areas for AI research to focus on to improve the intelligence of AI systems. They will present their study on July 17 at the US Artificial Intelligence Conference in Bellevue, Washington.

Article from http://www.gizmag.com/ai-system-iq-four-year-old/28321/

20 Historic Black and White Photos Colorized

Article from twistedsifte

One of the greatest facets of reddit are the thriving subreddits, niche communities of people who share a passion for a specific topic. One of the Sifter’s personal favourites is r/ColorizedHistory. The major contributors are a mix of professional and amateur colorizers that bring historic photos to life through color. All of them are highly skilled digital artists that use a combination of historical reference material and a natural eye for colour.

When we see old photos in black and white, we sometimes forget that life back then was experienced in the same vibrant colours that surround us today. This gallery of talented artists helps us remember that :)

Below you will find a collection of some of the highest rated colorized images to date on r/ColorizedHistory.

 

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