Video and Image Search In the News
June 17, 2015
There’s been much activity around video and image search lately. Is it all public-relations hype, or is there really progress to celebrate? Here are a few examples that we’ve noticed recently.
Fast Company reports on real-time video-stream search service Dextro in, “This Startup’s Side Project Scans Every Periscope Video to Help You Find the Best Streams.” Writer Rose Pastore tells us:
“Dextro’s new tool, called Stream, launches today as a mobile-optimized site that sorts Periscope videos by their content: Cats, computers, swimming pools, and talking heads, to name a few popular categories. The system does not analyze stream text titles, which are often non-descriptive; instead, it groups videos based only on how its algorithms interpret the visual scene being filmed. Dextro already uses this technology to analyze pre-recorded videos for companies … but this is the first time the two-year-old startup has applied its algorithms to live streams.”
Meanwhile, ScienceDaily reveals an interesting development in, “System Designed to Label Visual Scenes Turns Out to Detect Particular Objects Too.” While working on their very successful scene-classification tool, researchers at MIT discovered a side effect. The article explains that, at an upcoming conference:
“The researchers will present a new paper demonstrating that, en route to learning how to recognize scenes, their system also learned how to recognize objects. The work implies that at the very least, scene-recognition and object-recognition systems could work in concert. But it also holds out the possibility that they could prove to be mutually reinforcing.”
Then we have an article from MIT’s Technology Review, “The Machine Vision Algorithm Beating Art Historians at Their Own Game.” Yes, even in the highly-nuanced field of art history, the AI seems to have become the master. We learn:
“The challenge of analyzing paintings, recognizing their artists, and identifying their style and content has always been beyond the capability of even the most advanced algorithms. That is now changing thanks to recent advances in machine learning based on approaches such as deep convolutional neural networks. In just a few years, computer scientists have created machines capable of matching and sometimes outperforming humans in all kinds of pattern recognition tasks.”
Each of these articles is an interesting read, so check them out for more information. It may be a good time to work in the area of image and video search.
Cynthia Murrell, June 17, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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Video and Image Search In the News : Stephen E. Arnold @ Beyond Search