Image Recognition Is Changing Interactive Marketing
As a creative marketer, you’re likely on the lookout for innovative ways to reach your audience at all times. There’s a new kid on the block that just might revolutionize interactive marketing, and it’s called image recognition.
While visual recognition enabled by machine learning has been around for awhile, its potential to boost marketers’ efforts is being discovered only lately. Unlike text, which is easily searchable, visual information online has remained a ‘black box.’ But consumers today share an enormous amount of visual data, which marketers struggle to understand.
How can image recognition help you as an interactive marketer?
Image recognition can help brands make sense of the data contained in the ‘visual web.’ Visual listening through machine learning arms marketers with a powerful tool that they’re seeking relentlessly: relevance of content for their audiences.
Because of the technical capabilities it presents, computer recognition of images allows for creative forms of visual storytelling across media too. This offers powerful methods for engaging people and immersing them in new types of interaction with brands. Through effortless profiling based on the social visual content people share online rather than extensive questionnaires, image recognition opens new doors for personalization.
Here are three top ways in which image recognition is bringing unseen advancements for interactive marketing - and how your creative campaigns can hop on the new tech wave.
Visual listening is on the rise for brands
Until recently, a textual understanding of social media and the content shared there by users was the status quo. But with billions of images created and distributed online daily, brands now realize that social listening focusing on text only is ineffective and simply outdated. The rise of visual social networks like Pinterest, Instagram, Snapchat and Tumblr, as well as the wide use of visuals on Facebook, Twitter and LinkedIn, further pushes this understanding.
Marketers need to analyze enormous quantities of visual material to grasp how people use the currency of ‘visual’ online. While some of them have tried to make sense of images via metadata, this still has not shed enough light on the vast spaces of the visual web.
Enter visual listening. This new method for understanding photos and graphics online is enabled by image recognition AI. It enables brands to mine visual content that their audiences are sharing and engaging with. In this way, they can identify patterns, analyze trends and gain valuable business insights. Marketers can use this data to track how visual posts spread online, what type of visuals gain the most attention, and who is engaging with their visual content. And that’s only the tip of the iceberg.
Simply put, visual listening allows marketers to understand how people consume and create visuals in relation to brands. It provides them with a way to read consumers’ emotions and reactions from the visual data they share. This is also a tool for them to discover and benefit from user generated content that promotes the brand and to identify influencers that can be instrumental in their marketing efforts.
For brand protection, image recognition is a handy tool for monitoring how copyrighted visual material is handled online. Brands can also gain insights into their competitors’ visual presence, which can inform their own campaigns.
With image recognition, personalization can flourish
The applications of image recognition for marketing is not limited to visual listening. In fact, it can fuel powerful personalization, so brands can better reach and engage their audiences.
Creative marketing campaigns based on image recognition analysis can be highly targeted and thus can make a real impact. Through gathering insights from visuals, marketers can learn more about the preferences of different target groups. Based on this, they can craft content that engages people better because it’s relevant and personalized. `Instead of using tedious questionnaires that people avoid, it allows brands to learn more about people’s preferences without asking questions. This is done by analyzing the visual content that users have shared online. It empowers brands to tell engaging stories at the right points of contact with people. KIA Motors created an interactive campaign for 36 lifestyles to match its new KIA K5 (Optima) .
How is image recognition used in the financial industry
The Commonwealth Bank of Australia equipped its mobile app with image recognition software that allows people looking for a new house to take a photo of their dreamed home. By analyzing the picture, the app provides them with information on prices, taxes and other details. Furthermore, the app analyzes their personal financial data to inform them of mortgage options for purchasing the house.
How image recognition created interactive in-store campaigns
Image recognition has a place in the direct shopping experience of consumers by fueling contextual marketing and template matching. Some stores and shopping apps have already integrated image recognition capabilities. People can take a photo of an item they’d like to purchase. Then they receive information about it, locations where they can get it, or even a mobile-optimized page to order it directly.
Big brands like Macy’s and Neiman Marcus have embedded image recognition in dedicated apps. Customers take photos of clothes and accessories they like, and the app automatically suggests items from the brands’ inventory. Other online and brick-and-mortar retailers have also adopted image recognition to engage customers and make their shopping seamless.
A well-known example of image recognition in a consumer app is Vivino. People snap a photo of a wine label to get ratings and details about the brand and sort. The app has turned into a popular helper for making an informed wine choice.
How is image recognition used for access control
Another cool example of image recognition for an experimental creative campaign is Imagga’s Hipster Bar hack. During the WdW Festival in 2015, the Rotterdam-based artist Max Dovey hosted an installation called the Hipster Bar. Using Imagga’s image recognition, the camera at the door of the bar would take a photo of the person who wants to enter. Then it would juxtapose it against a database of photos of hipsters. If the person matches the style, they’d be able to enter the bar. The concept used for this art case can be applied in a number of marketing purposes. It is based on custom training of the AI by providing it with relevant visual data.
As these three prominent applications of image recognition illustrate, it holds impressive potential for a number of interactive marketing initiatives. Analysis of previously untapped visual data can inform marketers and help them craft powerful campaigns and on-spot content.
Now that you know what image recognition can do for your marketing would you like to give it a try?
What's In a Color? The Basics About Image Recognition Color Extraction
Image recognition is bringing revolutionary changes to the ways in which we consume and process information online. Deeply integrated into web pages and apps, it allows us to make sense of visual data in small and large quantities alike as we’ve never been able to do before.
The applications of image recognition are diverse and empowering. Color extraction is one of the most significant and game-changing capabilities offered by computer vision. The possibility to identify and analyze the colors in images gives numerous possibilities to businesses to better use their visual libraries, monetize them, and even increase sales of in-store products.
How does color extraction through image recognition work? The color API enables analysis of visuals in terms of the colors they contain. It determines the five most prominent colors that are present in an image. Then they can be exported as hex code, RGB triple, specific color name, and parent color name. This makes them easy to use for, say, keyword tagging and categorization.
Let’s delve into the capabilities of color extraction and how you can put it to use for your business.
What does color extraction offer?
The color extraction technology enabled by image recognition has a diverse business and user applications. But how does it make the online experience better?
Color extraction from images allows for keyword tagging of visuals by color. This makes it possible to easily navigate large databases containing visuals. As color differentiation is essential for categorizing images, it allows for searching and browsing based on color tagging.
Multi-color search is a typical part of color extraction technologies as well. Through using it, you can conduct more complicated search of colors. This means you can identify complex objects that contain more than one dominant color. It also enables multi-color filtering of image search in databases and websites hosted in a color palette functionality.
With powerful color extraction APIs, you can also identify the colors in the foreground and background of an image. In this way, you can remove the background if needed, or unnecessary elements from the foreground. This allows for more flexibility, so you can focus only on the objects on the image, or on the setting behind them.
How can you use color extraction in your business?
The possibilities that color extraction presents are fascinating, but the best part is that they can boost user experience and product visibility for your business.
Let’s consider how an e-commerce website selling clothes can benefit from color extraction. The color API can analyze the photos of all garments and provide the five predominant colors for each item. The color keywords are then attributed to the product.
When a buyer is searching in the online store for, say, rocker jeans in black, they can just filter the products on the website by the color of their preference. With Imagga’s color API, the user can even type in the exact name of the color they’re looking for. This is especially useful for color blind people, as the color extraction would allow for differentiation of shades and nuances that they would not be able to make otherwise.
Take virtual wedding planners as another example for the commercial uses of color extraction. By using a coloring API, they can offer automated color analysis for couples who want to decide on their wedding color palette. It would allow for uploading a photo with the color preferences of the client. On the basis of its analysis, the color extraction tool would offer similar color combinations.
Another great use of color extraction is suited for image-based platforms such as Pinterest. If multi-color search is integrated with fashion and design inspiration websites and apps, this would allow users to conduct a color search of immensely large visual databases. They would be able to create groups of images and albums categorized by colors. Besides significantly improving the user experience, this feature can also be monetized by businesses. The color search and categorization can be used by a wide variety of platforms such as design, photography, painting, interior design, and more.
Learn how you can integrate color extraction with ease
Integrating color extraction in your website or app doesn’t need to be complicated. Imagga’s color extraction API is offered as a service. You don’t have to install anything. You just send HTTP requests to our servers in the cloud and get thousands of images processed in a matter of hours.
What are your top examples of using online color extraction? We’d love to hear about your creative approach in the comments below.
How Image Recognition Powers the Stock Photo and Video Industry Today
Stock photography and videography have become popular work options for many visual professionals seeking freedom and creative expression. At the same time, royalty-free images and videos are constantly needed across industries: from advertising agencies to the marketing departments of a wide range of businesses across the globe.
Besides creating the visual assets, stock contributors often need to take care of the technical details such as uploading, organizing and tagging with keywords the images and videos. Since success in the industry often means developing huge visual databases, this process can get time-consuming and tedious.
Forward-looking stock websites have started embedding image recognition AI in their platforms to address the need for faster and more efficient keyword tagging. Tools like Qhero use image recognition to offer intelligent keywording to stock contributors of major stock websites like iStock. Using Qhero is very easy, simply upload your photos and they choose for which one you want to receive AI generated tags based on image recognition. Then you can choose the keywords that match the description of the image or simply validate them all.
How accurate are Artificial Intelligence suggested keywords?
Until recently, it was seen that image recognition cannot offer the high level of precision in keyword tagging that is expected. This led to the gradual adoption of computer vision for stock photo and video tagging. Today, the power that AI image tagging offers has changed this perception in the field. More and more stock photography and videography platforms are embedding AI to facilitate the process of keyword metadata enhancement. Here’s how the advancements in image recognition are fueling the progress in the stock photo industry.
Keyword suggestions for stock contributors
One of the main uses of image recognition AI is to help stock photo and video contributors describe their content with automatic keyword suggestions. Artificial Intelligence identifies the main objects, backgrounds, and themes in a visual. Then it generates a set of keywords with very high accuracy.
The approach to keyword tagging adopted by stock websites is mostly semi-automated. Image recognition technologies like Imagga suggest relevant keywords. The contributor has to manually approve them so that they are attributed to the visual.
Keyword suggestion powered by image recognition AI can save enormous amounts of time for stock contributors uploading royalty-free photos, vector graphics, and videos. They often upload large volumes of visuals, which need to be properly tagged with keywords, so that they are discovered by buyers. It usually takes time for contributors to get a hold of good keyword tagging that will bring them sales. They also need to predict buying trends and adjust keyword tagging accordingly. By automating with an advanced technology like computer vision, this process can be facilitated and improved.
Additionally, stock photography and videography websites have specific requirements on the number, quality, and theme of the tags. Contributors have to comply with these rules in order for a visual to go through the website’s approval process. This can mean hundreds of hours of manual tagging, which makes automated keyword suggestions so precious for contributors.
Besides saving a ton of time, keyword suggestions can save contributors money as well. Some professional stock photographers are paying specialized agencies to manually tag their images with relevant keywords. With image recognition providing automatic keyword suggestions, they have a viable option to switch back to handling the tagging on their own.
The AI-powered keywording functionality for QHero created by Imagga is a good illustration of how image recognition can speed up tagging of visuals. With the help of our API, intelligent keyword suggestions are now offered in the QHero image uploader software to assist stock contributors with their keyword tagging.
Robust search options for stock users
Keywords are important metadata not only for search engines but also for the stock photo and video websites. Adequate keyword tagging helps platforms categorize their visual content and make it discoverable for buyers. By providing AI-powered search options, image recognition can make discovering images faster and more efficient.
Image recognition can enhance visuals’ indexing for stock websites, so content can become more easily discoverable. Stock contributors get automatic keyword suggestions that complement their manual keyword tagging. The keyword attribution process is thus enriched and can be more widely encompassing, as a greater amount of relevant keywords can be attributed to the stock visuals.
Improved discoverability of stock visual content brings immense benefits for all parties involved. Stock photo users can find the visuals they need seamlessly. This makes their experience with stock platforms better. This, in turn, can help boost stock photo sales.
Content that was underperforming because of poor keyword tagging gets better exposure. Newly uploaded visuals automatically get more advanced tagging. This translates into direct financial benefits for both contributors and stock platforms, which earn a percentage of every sale.
You can see a good example of how image recognition was integrated into a popular free stock photo platform in Imagga’s Unsplash case study. Unsplash is a website that offers royalty-free images for free download. Photo contributors may not always have the time to add enough keywords to their images, as they don’t get direct payment. With Imagga’s technology, this process was automated.
Our computer vision API allowed Unsplash to offer advanced search options for its users. Besides traditional search of photos by keyword, all photos are discoverable by categories such as nature, people, and the like. Today more than five million searches are conducted with an Imagga-powered Unsplash search capability.
The creative market today can greatly benefit from AI-powered keyword tagging. Stock photographers and videographers can save time and money in the tedious process of attributing keywords to their visual assets. In the same time, stock buyers can get improved search options and easier content discovery.
What are the benefits of image recognition in your project? We’d love to get your insights in the comments below!
11 Female Researchers Who Made a Big Impact on Artificial Intelligence
What do you imagine when you hear about an ‘artificial intelligence researcher’?
We bet that for most of us, the cliche image that comes to mind is a serious-looking professor with very thick rim glasses. But don’t worry: your concepts are about to change.
In the exciting field of AI and machine learning, women today have carved their rightful place through their astounding achievements. From Asia to the U.S., female researchers have made considerable contributions to advancements in artificial intelligence.
Here are 11 of the most impressive women in AI today, and why their work has made a difference in the field.
#1. Fei-Fei Li
A name that certainly belongs to the list of outstanding women in AI is Fei-Fei Li. She is the Director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. Fei-Fei Li got her Ph.D. from the California Institute of Technology. Currently, she is an Associate Professor at the Computer Science Department at Stanford University.
Her most prominent work is in the field of computer vision. Fei-Fei Li is a part of scientific teams that have made advancements in image recognition software. Their experiments have taken image recognition beyond simple identification of objects in visuals - to recognizing scenes and generating descriptions of them in English.
The team of researchers achieved this through working with neural networks, software programs that mimic the way the human brain operates. This allows AI to train itself into recognizing patterns without input from researchers. In the case of Fei-Fei Li’s team, they used neural networks that could recognize what’s in the images and could then condense this in language forms.
#2. Jia Lin
Another bright mind at the Department of Computer Science at
Stanford University is Jia Li. she received her Ph.D. degree at the same university.
Jia Li has been the leader in the Visual Computing and Learning Group at Yahoo! Labs for three years. Later on, she became the Head of Research at Snapchat. In 2016, Google got her on board of its Google Cloud Machine Learning group.
Besides her academic research in computer vision, machine learning, and mobile vision, Jia Li is known for her innovations at Snapchat. Under her guidance, the app introduced a new type of emojis that interact with the environment the user is shooting.
#3. Maria Petrou
The late Maria Petrou was a Greek-born scientist who focused her work on digital image processing. She got her Ph.D. degree from Cambridge University and later had a postdoctoral position at Oxford.
One of her most important contributions to the image recognition field was the ‘trace transformation,’ a technique used in the development of face recognition software. Her work was also focused on segmentation that allows easier identification of edges and simple forms in fuzzy visuals. Maria Petrou was also an active member of organizations like Women in Science and Engineering and the Women's Engineering Society.
#4. Ruzena Bajcsy
Ruzena Bajcsy got her first Ph.D. degree in 1967 from the Slovak Technical University. In 1972, she also received a Ph.D. in computer science from Stanford University. Currently, she is a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She is also the Director Emeritus of the Center for Information Technology Research in the Interest of Science (CITRIS).
Ruzena Bajcsy contributions to AI are in the fields of computer vision, robotics, and human modeling. She has published her research extensively in hundreds of papers. Among a number of awards and acknowledgments, she also received the 2013 IEEE Robotics and Automation Award for her work in computer vision and medical robotics.
#5. Ellen Hildreth
Ellen Hildreth obtained her Ph.D. at the Massachusetts Institute of Technology. She is a Professor of computer science at Wellesley College, specializing in computer vision and visual perception.
Ellen Hildreth is known for the Marr-Hildreth algorithm, which was invented by her and David Marr. It is a method used in image recognition for detecting edges in the visuals. In her teaching and research, she also has a substantial contribution in the field of vision that combines computer systems and learnings from neuroscience and psychology.
#6. Francesca Rossi
Moving to the European continent, Francesca Rossi is certainly a name worth mentioned. She is a Professor of Computer Science at the University of Padova in Italy, with a Ph.D. in Computer Science from the University of Pisa in 1993.
Currently, Francesca Rossi works at the IBM T.J. Watson Research Center. Her research interests are in the fields of constraint reasoning, preference modeling, and multi-agent systems. She is the author of hundreds of scientific research papers. Francesca Rossi is also one of the editors of Handbook of Constraint Programming. She has received a number of AI awards for her contributions.
#7. Corinna Cortes
Corinna Cortes is a Danish-born computer scientist. She received her Ph.D. in Computer Science from the University of Rochester. Today she is the Head of Google Research in New York. Her previous position was at AT&T Labs - Research, where she worked for more than 10 years as one of the leading researchers.
The main contributions of her work are in machine learning, and more specifically, in support vector machines and data mining. She received the Paris Kanellakis Theory and Practice Award for her contribution to support vector machines research and development. The AT&T Science and Technology Medal in 2000 was awarded to Corinna Cortes for her work in data mining.
#8. Catherine Havasi
Catherine Havasi received her Ph.D. degree in Computer Science from the Brandeis University. She developed an interest in artificial intelligence already in her childhood. Her inspiration came from Marvin Minsky’s book The Society of Mind.
Havasi is CEO and co-founder Luminoso, a company that develops AI and natural language processing to help businesses get more from their data. She was one of the creators of the MIT’s Open Mind Common Sense project, and also one of the people behind ConceptNet, an open-source natural language artificial intelligence program. Catherine Havasi was named one of the “100 Most Creative People in Business” in 2015 according to Fast Company.
#9. Cynthia Breazeal
Cynthia Breazeal received her Sc.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology where she became the creator of the Kismet robot. Today she is an Associate Professor of Media Arts and Sciences at MIT and the founder of the Personal Robots Group at the Media Lab.
Breazeal’s contributions to social robotics and human-robot interaction are definitely noteworthy. She is the author of hundreds of research articles in the field, as well as of the book Designing Sociable Robots. She is also the Chief Scientist at Jibo, Inc., the first family robot.
#10. Bin Yu
Bin Yu is another female researcher worth mentioning for her work in artificial intelligence. With a Ph.D. in Statistics from University of California at Berkeley, the Chinese-American researcher is a Professor of Electrical Engineering & Computer Science at the same university today.
Bin Yu is the author of more than 100 papers on topics as vast as machine learning, information theory, signal processing, remote sensing, and neuroscience. In 2006, she was the co-recipient of the Best Paper Award of IEEE Signal Processing Society.
#11. Danica Kragic
The Croatian-born Danica Kragic received her Ph.D. in Computer Science from the the Royal Institute of Technology, KTH, in Sweden. She is currently a Professor at the School of Computer Science and Communication at the same institution.
In 2016, Danica Kragic was awarded as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) because of her work in vision-based systems and robotic object manipulation. She is interested in building systems that interact with people in the most ‘human’ ways possible. She is leading the Computer Vision and Active Perception Lab at KTH, and is the director of Centre for Autonomous Systems. Many of her research projects are financially supported by EU grants, and she takes part in numerous EU science programs.