Cloudinary Blog

How to automatically tag and categorize photos according to their content

How to automatically tag and categorize photos by their content

Update - December 2015: The add-on described in this post is no longer available since ReKognition terminated their services. However, all features described here are still available via a different and even better add-on by Imagga. See Automatic image categorization and tagging with Imagga and the Imagga Auto Tagging Add-on documentation.

Drawing insights from user generated content can be very useful. If you allow users to upload images, you might want to better understand what their images contain. Whether a photo is of a landscape, people, animals, or nightlife, image processing and analysis can assist in further comprehension.

ReKognition scene categorization

Cloudinary takes care of the entire image management pipeline, from uploading, via dynamic image manipulation, to fast CDN delivery. You can use Cloudinary with our add-on for automatic scene categorization provided by ReKognition, a visual recognition solution developed by Orbe.us.

ReKognition logo The ReKognition scene categorization add-on analyzes scenes within photos and allows you to automatically classify your website’s images into a long list of potential categories.

Improve user experience, engagement and conversion

By automatically recognizing what images contain, developers can easily create pages or site sections based on images’ content (e.g. photos of animals, travel, special events, etc.). In addition, if your site supports a search option, you can have the search results include relevant images based on their visual content instead of just by their file names or user-assigned tags. These benefits can improve user experience on your site and increase engagement.

When you identify image content, you then know what images your users upload or search for and can target them with related content in order to enhance engagement and conversion. For example, if a user uploaded or searched for cat images, you can target him/her with actions, such as presenting pictures of other pets or any cats’ related content. Knowing the content of images on your site can have a direct impact on your online business.

How-to automatically categorize your images

Web and mobile developers can use Cloudinary's API to process images while uploading or process previously uploaded images.

For example, the image below was uploaded to Cloudinary using the code below. You can see that the image’s content is clearly of a dance party, so let’s see how it is categorized with the ReKognition add-on. Simply set the categorization upload parameter to rekognition_scene in order to perform the image’s content analysis and get the detected categories:

Cloudinary::Uploader.upload("my_dance_photo.jpg",
          :categorization => 'rekognition_scene', 
          :public_id => "dance_party")

Ruby:
cl_image_tag("dance_party.jpg")
PHP:
cl_image_tag("dance_party.jpg")
Python:
CloudinaryImage("dance_party.jpg").image()
Node.js:
cloudinary.image("dance_party.jpg")
Java:
cloudinary.url().imageTag("dance_party.jpg");
JS:
cloudinary.imageTag('dance_party.jpg').toHtml();
jQuery:
$.cloudinary.image("dance_party.jpg")
React:
<Image publicId="dance_party.jpg" >

</Image>
Angular:
<cl-image public-id="dance_party.jpg" >

</cl-image>
.Net:
cloudinary.Api.UrlImgUp.BuildImageTag("dance_party.jpg")
Android:
MediaManager.get().url().generate("dance_party.jpg");
iOS:
imageView.cldSetImage(cloudinary.createUrl().generate("dance_party.jpg")!, cloudinary: cloudinary)
Sample dance party uploaded photo

Cloudinary’s upload API's result shows the uploaded image's detected categories as well as their level of accuracy, provided as a score between 0 and 1, where 1 is 100% accurate. As you can see in the example below, the ReKognition add-on detected several categories for the image above, that were each given a score. The best category match was 'dance', with a 0.719 accuracy score.  

    {"public_id"=>"dance_party",
     "url"=>
      "https://res.cloudinary.com/demo/image/upload/v1417446163/dance_party.jpg",
     ...
     "info"=>
      {"categorization"=>
        {"rekognition_scene"=>
          {"status"=>"complete",
           "data"=>
            {"matches"=>
              [{"tag"=>"dance", "score"=>0.719},
               {"tag"=>"blonde", "score"=>0.3055},
               {"tag"=>"model", "score"=>0.24},
               {"tag"=>"night_club", "score"=>0.1859},
               {"tag"=>"hair", "score"=>0.1449}]}}}},
    }

Image categorization data can be stored on your side in order to organize your content and target user interest. You can then take actions such as linking the categorization data to users that upload categorically related images. Once the information is captured, a data analysis can take place generating insights, including users’ image preferences. Based on the information and analysis provided, you will be able to take proactive actions to enhance user engagement and conversion.

Automatic tagging with ReKognition scene categorization

Cloudinary allows you to tag uploaded images. Each image can be assigned one or more tags. This feature automatically assigns tags based on categories that are detected by the ReKognition add-on.

You can see below that we set the auto_tagging parameter to 0.15 for the dance party example image above, which means that auto tagging will use the detected categories above this threshold. You can set the threshold to a higher level if you want to be more strict regarding the detected categories you want to tag. As for our example, the tagging results were 'blonde', 'dance', 'model', and 'nightclub':

Cloudinary::Uploader.upload("my_dance_photo.jpg", 
      :categorization => 'rekognition_scene', 
      :auto_tagging => 0.15, 
      :public_id => "dance_party")

Results in:

    "tags" => ["blonde", "dance", "model", "night_club"]

With automatic tagging using ReKognition’s scene categorization add-on, you will be able to use Cloudinary's admin API in order to list and search for all images with specific tags.

Summary

Understanding user generated content can provide you with great insights, allow you to take actions such as creating dynamic content pages, and match content to user preferences. Cloudinary’s service together with the fully integrated ReKognition scene categorization add-on provides developers with the powerful ability to enhance their image content management as well as increase their online users’ engagement and conversion.

ReKognition add-on

The ReKognition scene categorization add-on is available with all Cloudinary plans, You can try it out with the add-on’s free tier. If you don't have a Cloudinary account yet, sign up for a free account here.

Update - December 2015: The add-on described in this post is no longer available since ReKognition terminated their services. However, all features described here are still available via a different and even better add-on by Imagga. See Automatic image categorization and tagging with Imagga and the Imagga Auto Tagging Add-on documentation.

Recent Blog Posts

How to Make Boomerang Video Effect With Cloudinary

When you see the term boomerang, what is the first thing that comes to mind?

A thrown tool made of wood that returns to its thrower? Another definition is reversal, logically portraying the aim of the tool itself. Based on this definition, the term boomerang videos” came into play to depict videos that loop back and forth.

Read more
Shortening the Development Cycle of Media-Related apps with Cloudinary

Currently, the Android platform boasts the highest demand for mobile solutions, as evidenced by Google’s announcement in 2017 that there were two billion monthly active Android devices, a number that is likely to increase in the years ahead. For app developers like you, now is the right time to build and release solutions for Android. you might have also noticed that a higher percentage of apps being developed nowadays are filled with visual media: images and videos.

Read more
Build Your Own Image Storyboard Android App

Globally, approximately two billion people now own smartphones, which also feature cameras capable of capturing photos and videos of a tonal richness and quality unimaginable even five years ago. Until recently, those cameras behaved mostly as optical sensors, catching light that determines the resulting image's pixels. The next generation of cameras, however, can blend hardware and computer-vision algorithms that apply to an image's semantic content, spawning creative mobile photo and video apps.

Read more
Cloudinary Delivers Simplified Image Management Workflow for Fairfax Media's Digital Transformation

Fairfax Media Limited [ASX:FXJ] is one of the largest media companies in Australia and New Zealand that engages audiences and communities via print and digital media. It includes recognizable mastheads including The Australian Financial Review, The Sydney Morning Herald and The Age. Fairfax Media operates numerous news and information websites, as well as tablet and smartphone apps, for online news sites.

Read more
Bleacher Report Scores with Real-Time Video Highlights Delivered by Cloudinary

Bleacher Report is a global digital destination for sports fans, creating and collaborating on content at the intersection of sports and culture. Owned by Turner, a division of Time Warner, Bleacher Report's website and social channels focus on sports culture for the next generation of fans. Bleacher Report also has a five-star mobile app and popular email newsletters, which are part of the company’s strategy for instantly delivering in-depth articles, results and video highlights personalized for users’ favorite teams, players and leagues.

Read more