Computer Vision Development Services|TensorFlow Development Services

Computer Vision Development Services|TensorFlow Development Services

Oodles Computer Vision Development Services facilitate accurate object detection and image processing with our function-specific OpenCV and TensorFlow Development Services

Large retailers across the country are shifting their focus purely on eCommerce, thanks to lockdown 2.0. For the eCommerce sector to reap the benefits of this booming phase, it must employ advanced technologies such as artificial intelligence (AI). Today, AI-powered computer vision services are beginning to optimize eCommerce sales and enhance customer’s shopping journeys by deploying machine learning algorithms. From object detection to personalized recommendations, computer vision applications for eCommerce is making giant leaps to scale the digital presence for brands across verticals.

Let’s explore some effective computer vision applications that are poised to contribute to achieving the vision of .2 trillion USD eCommerce sales in 2020.

Accelerating eCommerce Conversions with Computer Vision Applications

1) Automated Product Categorization Visually appealing and organized product images together constitute the most significant layer of any eCommerce portal. With millions of product images pouring into an eCommerce database every day, it becomes laborious to structure the data efficiently.

Here’s when artificial intelligence augments eCommerce capabilities with computer vision technologies. Powered by machine learning development services, computer vision streamlines visual classification of commercial products with the following techniques-

a) Object Detection In contrast to manual validation and uploading, machine learning offers cost and time-effective solutions for accelerating object detection of real-world images. By training models with variable correct and incorrect image inputs, eCommerce businesses can automate-

(i) Validation of inappropriate images

(ii) Matching the right images with respective products

(iii) Extraction of features from product images

A Clothing Attribute detection model prepared by the students of Stanford University.

b) Image Classification Image classification is a step ahead in processing eCommerce images and enhancing the overall efficiency of businesses. The high classification accuracy of machine learning models enables businesses to extract the following attributes from product images-

(i) Logo size and type

(ii) Variable color palettes

(iii) Prints and clothing patterns

(iv) Shapes of different parts like necks, sleeves, and more.

By harnessing ML models for image classification, eCommerce businesses can improve marketing campaigns, categorize products on sites, enhance customer experience significantly.

2) Seamless Visual Search In the age of smart IoT devices, consumer expectations are pegged high for a seamless and enhanced online shopping experience. Computer vision applications for eCommerce are beginning to match dynamic customer needs by searching for their desired products with a picture of the item.

eCommerce giants, including eBay, are eliminating keywords to offer visual search for optimizing the customer experience.

The process is called “visual search”, wherein object detection, image classification, and other inputs work together to find the visually similar result of the uploaded image.

The underlying deep learning technology enables customers to look for products at eCommerce portals by uploading images. The AI system can accurately match inputs received from object detection and image classification processes to find the right product results.

3) Personalized Recommendations While product recommendations are an age-long online sales phenomenon, the customer engagement technique has got a competitive advantage with AI. eCommerce giants are now exploring and proactively investing in deep learning to improve their recommendation engines and increase RPV. Though there are several kinds of recommendation techniques, deep learning is currently making strides in-

a) Collaborative filtering The neural network under collaborative filtering works on a simple logic of matching the preferences of a similar user base. It functions on the grounds that users buying similar items in the past will most likely buy certain items in the future.

Learn more: Computer Vision Applications for eCommerce

computer vision applications for ecommerce

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