Artificial intelligence (AI) based self-labelling system and method thereof

An Artificial Intelligence (AI) based self-labelling system comprising:

• a processor;
• and
• a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to:
• create, in real-time, image vectors from multimedia content captured via a camera;
• identify, by a trained AI model, a set of image vectors associated with at least one predefined category of interest from the image vectors;
• assign at least one dimension to each of the set of image vectors;
• determine, by the trained AI model, for a subset of image vectors within the set of image vectors, the availability of at least one relevant label from a plurality of pre-created labels, based on the at least one assigned dimension and associated attributes;
• receive a user input to assign a new label to the subset of image vectors, in response to determining non-availability of a relevant label from the plurality of pre-created labels;
• and
• perform, by the trained AI model, incremental learning based on the new label received from the user,
• wherein the processor-executable instructions further cause the processor to perform the incremental learning based on the new label by:
• determining a similarity index for the new label relative to at least one of the plurality of pre-created labels;
• and
• merging the new label with a pre-created label from the plurality of pre-created labels, wherein the similarity index of the pre-created label relative to the new label is the highest.

A non-transitory computer-readable medium storing computer-executable instructions for Artificial Intelligence (AI) based self-labelling, the stored instructions, when executed by a processor, cause the processor to perform operations comprises:

• creating, in real-time, image vectors from multimedia content captured via a camera;
• identifying, by a trained AI model, a set of image vectors associated with at least one predefined category of interest from the image vectors;
• assigning at least one dimension to each of the set of image vectors
• determining, by the trained AI model, for a subset of image vectors within the set of image vectors, the availability of at least one relevant label from a plurality of pre-created labels, based on the at least one assigned dimension and associated attributes;
• receiving a user input for assigning a new label to the subset of image vectors, in response to determining non-availability of a relevant label from the plurality of pre-created labels;
• and
• performing, by the trained AI model, incremental learning based on the new label received from the user,
• wherein, to perform the incremental learning based on the new label, the computer-executable instructions further configured for:
• determining a similarity index for the new label relative to at least one of the plurality of pre-created labels;
• and
• merging the new label with a pre-created label from the plurality of pre-created labels, wherein the similarity index of the pre-created label relative to the new label is the highest.