June 25, 2015
The main point of an advertisement is to get your attention and persuade you to buy a good or service. So why would ads be hiding themselves in a public venue? Gizmodo reports that in Russia certain ads are hiding from law enforcement in the article: “This Ad For Banned Food In Russia Itself From The Cops.” Russian authorities have banned imported food from the United States and European Union. Don Giulio Salumeria is a Russian food store that makes its income by selling imported Italian food, but in light of the recent ban the store has had to come up with some creative advertising:
“Websites are already able to serve up ads customized for whoever happens to be viewing a page. Now an ad agency in Russia is taking that idea one step further with an outdoor billboard that’s able to automatically hide when it spots the police coming.”
Using a camera equipped with facial recognition software programmed to recognized symbols and logos on officers’ uniforms, the billboard switches ads from Don Giulio Salumeria to another ad advertising a doll store. While the ad does change when it “sees “ the police coming, they still have enough time to see it. The article argues that the billboard’s idea is more interesting than anything. It then points out how advertising will become more personally targeted in the future, such as a billboard recognizing a sports logo and advertising an event related to your favorite team or being able to recognize your car on a weekly commute, then recommending a vacation. While Web sites are already able to do this by tracking cookies on your browser, it is another thing to being tracked in the real world by targeted ads.
Whitney Grace, June 25, 2015
June 23, 2015
MIT did not discover object recognition, but researchers did teach a deep-learning system designed to recognize and classify scenes can also be used to recognize individual objects. Kurzweil describes the exciting development in the article, “MIT Deep-Learning System Autonomously Learns To Identify Objects.” The MIT researchers realized that deep-learning could be used for object identification, when they were training a machine to identify scenes. They complied a library of seven million entries categorized by scenes, when they learned that object recognition and scene-recognition had the possibility of working in tandem.
“ ‘Deep learning works very well, but it’s very hard to understand why it works — what is the internal representation that the network is building,’ says Antonio Torralba, an associate professor of computer science and engineering at MIT and a senior author on the new paper.”
When the deep-learning network was processing scenes, it was fifty percent accurate compared to a human’s eighty percent accuracy. While the network was busy identifying scenes, at the same time it was learning how to recognize objects as well. The researchers are still trying to work out the kinks in the deep-learning process and have decided to start over. They are retraining their networks on the same data sets, but taking a new approach to see how scene and object recognition tie in together or if they go in different directions.
Deep-leaning networks have major ramifications, including the improvement for many industries. However, will deep-learning be applied to basic search? Image search still does not work well when you search by an actual image.
June 10, 2015
Online shopping is supposed to drive physical stores out of business, but that might not be the case if online shopping is too difficult. The Ragtrader article, “Why They Abandon” explains that 45 percent of Australian consumers will not make an online purchase if they experience Web site difficulties. The consumers, instead, are returning to physical stores to make the purchase. The article mentions that 44 percent believe that traditional shopping is quicker if they know what to look for and 43 percent as prefer in-store service.
The research comes from a Rackspace survey to determine shopping habits in New Zealand and Australia. The survey also asked participants what other problems they experienced shopping online:
“42 percent said that there were too many pop-up advertisements, 34 percent said that online service is not the same as in-store and 28 percent said it was too time consuming to narrow down options available.”
These are understandable issues. People don’t want to be hounded to purchase other products when they have a specific item in mind and thousands of options are overwhelming to search through. Then a digital wall is often daunting if people prefer interpersonal relationships when they shop. The survey may pinpoint online shopping weaknesses, but it also helps online stores determine the best ways for improvement.
“ ‘This survey shows that not enough retailers are leveraging powerful and available site search and navigation solutions that give consumers a rewarding shopping experience.’ ”
People shop online for convenience, variety, lower prices, and deals. Search is vital for consumers to narrow down their needs, but if they can’t navigate a Web site then search proves as useless as an expired coupon.
May 15, 2015
Image search means having software which can figure out from a digital photo that a cow is a cow. In more complex photos, the software identifies what it can. I recall one demonstration which recognized me as a 20 year old criminal. Close but no cigar.
I received an email from a former clandestine professional. The link provided informed me that Baidu was better at image recognition than the Google. The alleged error rate is 4.58 percent. I love the two decimal accuracy.
Not to be outdone, WolframAlpha is in the image recognition game as well. Navigate to “Wolfram Alpha Image Identification Identifies Steven Wolfram as Podium.” The write up points out:
Speaking of which, a picture of Steven Wolfram returned the answer ‘podium’. So no recognition for the creator. Unfortunately, it couldn’t identify a map of France at all and just came back with a big question mark. Sorry, France.
You can try the system at this page.
I uploaded the image of the cover of my new CyberOSINT study. The system returned this result:
My book cover is a a piece of electronic equipment that mixes two or more input signals to give a single output signal.
I did not know that. I thought it was a book cover with a blue hand.
Stephen E Arnold, May 15, 2015
April 26, 2015
Need a GIF file? Check out “5 GIF Search Engines & Tools You Haven’t Heard Of Yet.” Searching for GIFs using some Web search engines can yield interesting results.
Stephen E Arnold, April 26, 2015
April 8, 2015
Anyone interested in the mechanics behind image search should check out the description of PicSeer: Search Into Images from YangSky. The product write-up goes into surprising detail about what sets their “cognitive & semantic image search engine” apart, complete with comparative illustrations. The page’s translation seems to have been done either quickly or by machine, but don’t let the awkward wording in places put you off; there’s good information here. The text describes the competition’s approach:
“Today, the image searching experiences of all major commercial image search engines are embarrassing. This is because these image search engines are
- Using non-image correlations such as the image file names and the texts in the vicinity of the images to guess what are the images all about;
- Using low-level features, such as colors, textures and primary shapes, of image to make content-based indexing/retrievals.”
With the first approach, they note, trying to narrow the search terms is inefficient because the software is looking at metadata instead of inspecting the actual image; any narrowed search excludes many relevant entries. The second approach above simply does not consider enough information about images to return the most relevant, and only most relevant, results. The write-up goes on to explain what makes their product different, using for their example an endearing image of a smiling young boy:
“How can PicSeer have this kind of understanding towards images? The Physical Linguistic Vision Technologies have can represent cognitive features into nouns and verbs called computational nouns and computational verbs, respectively. In this case, the image of the boy is represented as a computational noun ‘boy’ and the facial expression of the boy is represented by a computational verb ‘smile’. All these steps are done by the computer itself automatically.”
See the write-up for many more details, including examples of how Google handles the “boy smiles” query. (Be warned– there’s a very brief section about porn filtering that includes a couple censored screenshots and adult keyword examples.) It looks like image search technology progressing apace.
Cynthia Murrell, April 08, 2015
Stephen E Arnold, Publisher of CyberOSINT at www.xenky.com
March 24, 2015
I read “Images That Fool Computer Vision Raise Security Concerns.” I found the write up a reminder that the marketing and venture capitalists’ hype are one thing. Real world software performance is another thing.
The article states:
Cornell researchers have found that computers, like humans, can be fooled by optical illusions, which raises security concerns and opens new avenues for research in computer vision.
The passage I highlighted in a mellow yellow says:
But computers don’t process images the way humans do, Yosinski [a Cornell wizard] said. “We realized that the neural nets did not encode knowledge necessary to produce an image of a fire truck, only the knowledge necessary to tell fire trucks apart from other classes,” he explained. Blobs of color and patterns of lines might be enough. For example, the computer might say “school bus” given just yellow and black stripes, or “computer keyboard” for a repeating array of roughly square shapes.
It turns out that this diagram looks exactly like a penguin.
The smart software sees the abstraction as what most grade school children know as a lovable penguin. I did not smell a penguin until after I left grade school. Someone should have warned me.
And the challenge? I have no comment about the expectations of a government professional who relies on image recognition as part of an on going investigation.
Stephen E Arnold, March 24, 2015
February 17, 2015
In order to build a fantastic Web site these days, you need eye-catching graphics. While creating a logo can be completed with Fiverr, making daily images for your content feed is a little bit more difficult. It is not cost efficient to hire a graphic designer for every image (unless you have deep pockets), so it helps to have an image library to retrieve images. The problem with typing in image library into a search engine means you have to sift through results and assess each possible source.
Graphic designer Ash Stallard-Phillips collected “25 Awesome Sites With Stunning Free Stock Photos.” He rounded up the image libraries, because:
“As a web designer myself, I always find it handy to have an image library that I can use for dummy images and testing. I have compiled a list of the best sites offering free stock photos that you can use for your projects. “
Ash evaluates each resource, listing the pros and cons. Many of the image Web sites he lists are ones we have not used before and will be useful as we create content. There is an increase in the number of articles like Ash’s on the Internet and they are not just for photo libraries. They are lists that have tons of helpful information that you would usually have to sift through search results for. It saves time on searching and the evaluation process.
January 19, 2015
Watson has been going to town in different industries, putting to use its massive artificial brain. It has been working in the medical field interpreting electronic medical record data. According to Open Health News, IBM has used its technology in other medical ways: “IBM Research Scientists Investigate Use Of Cognitive Computing-Based Visual Analytics For Skin Cancer Image Analysis.”
IBM partnered with Memorial Sloan Kettering to use cognitive computing to analyze dermatological images to help doctors identify cancerous states. The goal is to help doctors detect cancer earlier. Skin cancer is the most common type of cancer in the United States, but diagnostics expertise varies. It takes experience to be able to detect cancer, but cognitive computing might take out some of the guess work.
Using cognitive visual capabilities being developed at IBM, computers can be trained to identify specific patterns in images by gaining experience and knowledge through analysis of large collections of educational research data, and performing finely detailed measurements that would otherwise be too large and laborious for a doctor to perform. Such examples of finely detailed measurements include the objective quantification of visual features, such as color distributions, texture patterns, shape, and edge information.”
IBM is already a leader in visual analytics and the new skin cancer project has a 97% sensitivity and 95% specificity rate in preliminary tests. It translates to cognitive computing being accurate.
Could the cognitive computing be applied to identifying other cancer types?
January 8, 2015
Image search is a touchy subject. Copyright, royalties, privacy, and accuracy are a huge concern for image holders and searchers. People are scouring the Internet for images they can freely use without problems, but often times the images have a watermark or are so common they are mediocre. Killer Startups points to a great new startup that could revolutionize how people find pictures: “Today’s Killer Startup: Compfight.”
Compfight is an image search engine comparable to Flicker, except it is faster and uses features similar to the advanced search function on Google.
“The site also lets you specify if you’re looking only for Creative Commons licensed images or ones to use commercially. If you’re new to this kind of image use, Compfight even provides a handy little guide on how to cite your sources properly. Last and probably least, Compfight also provides access to professional stock photos, starting as low as $1 per image.”
Developers are still trying to create the perfect image search and while it is a work in progress, Compfight shows we’re on the right path.