But with the help of technology in this visual age, images, videos are becoming more and more prevalent in today's lives. In the early days, social media was predominantly text-based but now technology has started to adapt according to the needs of people with impaired vision too. All thanks to modern technologies for their design, making navigating social media for giving a wonderful experience to also the visually impaired. Let's look at such one technology called image recognition which made life easier for people with impaired vision. Here are the 7 ways it can aid people.
Depending on which mobile browser you use, it might not be immediately obvious how to do a reverse image search from your mobile device. The most simple solution we've come up with is to download and use Google's free Chrome browser for iOS or Android. This particular browser has a quick and easy built-in method for carrying out reverse image searches. We're taking a look at how you can do this in this simple explainer. Google's "Search by Image" functionality is a useful feature that carries out a reverse image search, allowing you to look for related images by uploading an image or an image URL.
According to a Forbes blog post from May 2018, over 300 million images are uploaded to Facebook and 95 million images are uploaded to Instagram each day. There's a good reason for this new trend: Images are more memorable, more impactful, and easier to share than text. You don't have to translate them. A picture is worth a thousand words, after all. Ninety percent of what our brains process is visual.
Google is borrowing a few cues from Instagram and Pinterest to encourage more shopping in its search results. The internet giant is testing shoppable ads within image searches -- find a picture of your ideal desk and you can tap a shopping tag button to see basic details as well as a link to buy it. This only applies to sponsored ads, thankfully, so you don't have to worry about ads covering the images you want to see. The test is currently visible only to a "small percentage" of users who search for certain broader topics, such as home offices and abstract art. Whether or not it expands will likely depend on early results.
There was a time when automation used to only work with image name or property and that was the easiest and only way to check whether assertion is true or false. Mostly in older times and using selenium Webdriver it used to be some code like this....does it sound familiar? But times are changing and there are some smart tools that can help you work swiftly with images and you do not have to use older ways. Sometimes the scripts were written covering the Alt tag case or only checking the height or width or both of the image and that used to tell whether image exists on a web form or not. But here is the changing world.
London-headquartered BeMyEye has made another acquisition, its third in a little over three years. This time the retail execution monitoring service is purchasing Russian crowdsourcing and image recognition provider Streetbee. The acquisition will see BeMyEye launch "Perfect Shelf," which will use image recognition technology to lower the cost for consumer goods companies wanting to get "objective and actionable" in-store insights. These will typically include share of shelf and planogram compliance (the specific placement of products on a store shelf). More broadly, BeMyEye offers a platform to enable companies and brands to crowdsource various in-store data.
Google hasn't been shy about borrowing cues from Pinterest. Its latest effort, however, may be more transparent than others. The company has confirmed to TechCrunch that it's testing a new Image Search on desktop with vertical results that will seem familiar if you're regularly browsing Pinterest for ideas. Each image now has captions along with badges describing what those images entail, such as a product or a video. And it won't surprise you to hear that clicking on a picture provides much, much more than before.
A while back, I was reading an article posted on Facebook, about Clovis people found alive and well living in Florida, with a picture featuring tribesmen (see below.) The quality of the picture was poor, and the URL was very suspicious: baynews9.com.ddwg.clonezone.link, as to make it appear that it was from Baynews9.com. It turned out that the picture (and thus the whole story) was fake: these people are real people living in Peru, see here for a Youtube video about them.
Long, Yang (Northwestern Polytechnical University, Xi'an) | Liu, Li (Newcastle University, Newcastle upon Tyne) | Shen, Yuming (JD Artificial Intelligence Research (JDAIR), Beijing) | Shao, Ling (University of East Anglia, Norwich)
Instance-level retrieval has become an essential paradigm to index and retrieves images from large-scale databases. Conventional instance search requires at least an example of the query image to retrieve images that contain the same object instance. Existing semantic retrieval can only search semantically-related images, such as those sharing the same category or a set of tags, not the exact instances. Meanwhile, the unrealistic assumption is that all categories or tags are known beforehand. Training models for these semantic concepts highly rely on instance-level attributes or human captions which are expensive to acquire. Given the above challenges, this paper studies the Zero-shot Retrieval problem that aims for instance-level image search using only a few dominant attributes. The contributions are: 1) we utilise automatic word embedding to infer class-level attributes to circumvent expensive human labelling; 2) the inferred class-attributes can be extended into discriminative instance attributes through our proposed Latent Instance Attributes Discovery (LIAD) algorithm; 3) our method is not restricted to complete attribute signatures, query of dominant attributes can also be dealt with. On two benchmarks, CUB and SUN, extensive experiments demonstrate that our method can achieve promising performance for the problem. Moreover, our approach can also benefit conventional ZSL tasks.