Deep Indexer
Similarity and reverse search
How can I identify and label people without pre-trained face recognition?
use case: Duplicate detection
use case: Duplicate detection
The challenge
The larger the amount of visual data, the greater the likelihood that there will be duplicates among the images and videos. This not only costs storage space, but also discourages users from doing quick searches or using consistent data. How can DeepVA help filter out the duplicates in my data?
the solution
Using the Deep Indexer, duplicates can be automatically detected and located in your data set. The system can be used on-premise or in the cloud. If it is to be part of a workflow, integration is required. Via our RESTful API, it can be easily integrated in any existing system or workflow. Data protection requirements usually play a major role in this decision and should be considered.
What results can be obtained?
Increase the quality of your database by avoiding duplicates
Save valuable storage space by removing duplicates
Extend the storage of data in your media workflow with another quality control measure
faster data acquisition
COST REDUCTION
faster labelling
Duplicate detection
Using Face Index, each person in video and image material can be assigned a number, allowing them to be translated into the metadata afterwards.
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Related use cases
How can I identify and label people without pre-trained face recognition?
How can I efficiently find what I’m really looking for in my visual media material?
How can I recognize new speakers in my media assets in the future?
A product of «The Chainless».
Designed and engineered in Germany.
The Chainless GmbH
Wannerstraße 19
79106 Freiburg
Germany
The project has indirectly received funding from the European Commission’s Horizon 2020 Framework Programme through the STADIEM project (Grant Agreement 957321)
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