Community Blog Artificial Intelligence ( AI ) in Image Processing

Artificial Intelligence ( AI ) in Image Processing

In this article, we will discuss to use artificial intelligence (AI) and deep learning in image processing, image recognition, image classification and image search.

Effectively making use of unstructured data from large amounts of image and voice data has always been a challenge for data mining professionals. The processing of unstructured data usually involves the use of deep learning algorithms and these algorithms can be daunting for beginners. In addition, processing unstructured data usually requires powerful GPUs and a large amount of computing resources. This article introduces a method of image recognition using deep learning. This method can be applied to scenarios such as illicit image filtering, and object detection.

This experiment creates an image recognition model using the deep learning framework TensorFlow in Alibaba Cloud Machine Learning Platform for AI. The entire procedure takes about 30 minutes to complete. After the model training process, the system is able to recognize the bird in the following image, and return the word "bird":


This experiment can be created from the following TensorFlow image classification template:

classification template

If you choose to create the experiment from the template, replace the checkpoint path in both the parent and child TensorFlow components with your OSS paths and then run the experiment, as shown in the following figure:

OSS paths

You can refer to Image Classification with TensorFlow and get more skills.

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Alibaba Cloud Machine Learning Platform for AI: Image Classification by Caffe

This article introduces Caffe deep learning framework to perform complete image classification model training on Alibaba Cloud's Machine Learning Platform for AI.

The Image classification by Tensorflow section introduces how to use the TensorFlow framework of deep learning to classify CIFAR-10 images. This section introduces another deep learning framework: Caffe. With Caffe, you can complete image classification model training by editing configuration files.

Make sure that you have already read the Deep Learning section and activated deep learning in Alibaba Cloud Machine Learning Platform for AI (PAI).


This experiment uses a CIFAR-10 open-source dataset, containing 60,000 images with pixel dimensions 32 x 32. These images are classified into 10 categories: airplanes, automobiles, birds, cats, deer. dogs, frogs, horses, ships, and trucks. The following figure shows the dataset.


The dataset has already been stored in the public dataset in Alibaba Cloud Machine Learning Platform for AI in JPG format. Machine learning users can directly enter the following paths in the Data Source Path field of deep learning components:

  1. Testing data: oss://dl-images.oss-cn-shanghai-internal.aliyuncs.com/cifar10/caffe/images/cifar10_test_image_list.txt
  2. Training data: oss://dl-images.oss-cn-shanghai-internal.aliyuncs.com/cifar10/caffe/images/cifar10_train_image_list.txt

Trends and Innovations of Image Search in the Retail Industry

In this article, we discuss how image search technology can be applied to the retail industry in the form of mobile virtual assistant and targeted campaigns.

The rise of artificial intelligence (AI) and big data technologies have helped fuel innovations in the retail industry. Cutting-edge products, including Alibaba Cloud's Image Search and Machine Learning Platform for AI, have transformed the way customers interact with and shop for products. Customers no longer need to queue up in brick and mortar stores; they can also conveniently search for products by performing a quick image search.

However, the usage of image search technology is not limited to a simple product search on e-commerce platforms in the retail industry. In recent years, we have witnessed an increasing number of initiatives to provide new customer experience through the integration of simple image search service. This includes using image search for stock keeping unit (SKU) search as well as for matching products with source materials and or patterns.

This article shows how image search technology can be used in different areas other than e-commerce platform product search.

Mobile Virtual Assistants

An innovative application of image search technology in the retail industry is the development of mobile virtual assistants. This solution is intended to help customers to choose the right product with virtual assistance, reducing labor costs as well as optimizing stock keeping.

There is always an issue to promote new products in reseller shops. For example, the shops may not have enough assistants, the shop assistants may not have the latest product information, or they are not familiar to all items in the product line. Furthermore, products with similar packaging makes it challenging for customers and shop assistants to determine the right item.

These problems can be easily solved by simple app integrating with image search service. For example, we can provide a virtual assistance through a membership application or QR code. Through the application, the customer can take a picture of the product to retrieve product information including product features, pricing, comparison, as well as guidelines to choose the right items.

If customers have more questions, we can integrate online customer services (such as a chatbot or service center) to support the customers by providing personalized services. This flow can easily link to other follow up promotions such as coupon or product recommendations. Additionally, this approach can be one of the channels for O2O integration by allowing customers to purchase from online shops.

Image Recognition Using Edge Detection

Image recognition is a popular technology that can detect, understand, and distinguish images from one another.

Image recognition is a popular technology that can detect, understand, and distinguish images from one another.

How is it done?

Understanding the way we perceive objects and images has always been a hot topic for research. Researchers globally have observed that the human eye is very sensitive to the edges of an object. Typically, a person identifies an object by first determining the outline of the object and then processing this information in the visual cortex. Computer scientists have designed sophisticated image recognition systems by emulating the way we recognize images.

The example below is based on a paper by Adit Deshpande, a student at The University of California, titled A Beginner's Guide To Understanding Convolutional Neural Networks. In this paper, he introduces a simple algorithm as the basis of image recognition.

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For more information about IMG, see Image Processing.

For the complete code of IMG, see GitHub.


IMG uses standard HTTP GET. You can configure IMG parameters in QueryString of a URL.

If the ACL of an image object is private read and write, only authorized users are allowed for access.

Image processing access rules

In Image Service, URLs are accessed with standard HTTP GET requests, and all processing parameters are in the QueyString of the URL.

Request for thumbnails through processing parameters
If you want to have a source image processed and then returned, the following two formats are available:


Access through a third-level domain name: http://bucket./object?x-oss-process=image/action,parame_value

  1. Bucket: your Image Service channel.
    endpoint: the access domain name for a Bucket’s data center.
  2. Object: In Image Service, an Object is the basic data unit for operating images. It is the same as the Object specified for the OSS instance. The maximum size of a single Object (that is, each image) is 20 MB.
  3. action: the operation to be performed on the image.
  4. parame: the parameter which indicates the operation to be performed on the image.

Combination of multiple actions

Multiple actions are executed in sequence. For example, image/resize,w_200/rotate,90 has the effect of scaling down an image to 200 in width and then rotating the image 90 degrees.

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