Visual Search
The purpose of visual search is recognizing target objects on images. The main scope
nowadays is e-commerce
nowadays is e-commerce
Visual search is no longer just a theoretical concept – it is a growing part of the search landscape. Every day, visual search engines make heavy use of AI and machine learning to better perform image searches. They study shapes, sizes, colors, and patterns, and they learn to use these qualities to identify objects in the same way the human mind does. Today's consumers may not use visual search regularly, but they are ready to embrace
this new technology.
We are approached by services that want to enable their users to find and buy products that they saw somewhere and were able to photograph. After that, the service recognizes what kind of product is shown in the photo, and finding where to buy it on the Internet offers the user to make this purchase or indicates in which stores you can find it.
The good quality of such services requires a good variety in the training sample for the neural network, especially given the fact that similar things look exactly the same in different regions.
this new technology.
We are approached by services that want to enable their users to find and buy products that they saw somewhere and were able to photograph. After that, the service recognizes what kind of product is shown in the photo, and finding where to buy it on the Internet offers the user to make this purchase or indicates in which stores you can find it.
The good quality of such services requires a good variety in the training sample for the neural network, especially given the fact that similar things look exactly the same in different regions.
Tagias Manager helps you create a good technical task for the data or markup that needs to be performed. So that you get a training dataset that gives real results and a low error rate.
VISUAL
CASES
Use cases
Image annotation
Shopping applications
Automotive driving
Face
reсognition
reсognition
Analyzing a great number of pictures trained AI can find the specifiс item by image
Collecting the information about the environment for safe automotive driving
Neural networks are learned to recognize certain patterns of meaningful objects
Bounding boxes
The most common type of data annotation. Bounding boxes are mainly intended to detect objects, delineating their borders with transparent rectangles. Bounding boxes are applied in almost all industries of the data labelling market.
Classification
This method is used to determine the presence of an object in the image. A specific label is assigned to each image. Image tagging helps train AI to recognize objects' classes.
Keypoints
This type of annotation uses dots across an image to identify an object or its details/parts. Keypoints are applied mostly with facial recognition, body parts, and postures.
Polygons
Polygons are used to determine an object's shape and location with maximum precision while avoiding additional noise. Polygon annotation is applied in almost all industries.
Lines
Lines annotation is used for detecting and recognizing lanes. This type of annotation is applied mostly in the automotive industry to make self-driving cars safe.
Semantic segmentation
A data labelling method where objects are delineated in the image at the pixel level, depending on class. It's called semantic segmentation because each pixel has semantic meaning. Segmentation is usually applied in the automotive industry.