Automated content analysis and annotation

A system comprising:

• a computing platform having a processing hardware and a system memory storing a software code;
• the processing hardware configured to execute the software code to:
• receive content including an image having a plurality of image regions at least some of which form a sequence of image regions sharing a relationship in which a perspective from which the sequence of image regions are drawn does not change, but wherein one or more local features portrayed within the at least one sequence of bounded image regions change;
• determine, using a machine learning (ML) model, a respective boundary of each of the plurality of image regions to identify a plurality of bounded image regions, wherein the ML model is trained using training data including synthetic images generated from previously detected image regions of other real images;
• identify, within each of the plurality of bounded image regions, respective one or more local features and respective one or more global features;
• confirm or modify, using the relationship, for each of the bounded image regions corresponding respectively to the image regions sharing the relationship, the identified respective one or more local features and the identified respective one or more global features to provide confirmed or modified respective one or more local features and confirmed or modified respective one or more global features;
• and
• annotate each of the bounded image regions corresponding respectively to the image regions sharing the relationship, using the confirmed or modified respective one or more local features and the confirmed or modified respective one or more global features to provide an annotated content.

A method for use by a system including a computing platform having a processing hardware, and a system memory storing a software code, the method comprising:

• receiving, by the software code executed by the processing hardware, content including an image having a plurality of image regions;
• determining, by the software code executed by the processing hardware and using a machine learning (ML) model, a respective boundary of each of the plurality of image regions to identify a plurality of bounded image regions, wherein the ML model is trained using training data including synthetic images generated from previously detected image regions of other real images;
• identifying, by the software code executed by the processing hardware within each of the plurality of bounded image regions, respective one or more local features and respective one or more global features, wherein the plurality of bounded image regions include at least one sequence of bounded image regions sharing a relationship in which a respective perspective from which the at least one sequence of bounded image regions are drawn does not change, but wherein the respective one or more local features portrayed within the at least one sequence of bounded image regions change;
• identifying, by the software code executed by the processing hardware within each of the plurality of bounded image regions, another one or more local features based on a comparison with corresponding one or more local features identified in each of one or more other bounded image regions;
• and
• annotating, by the software code executed by the processing hardware, each of the plurality of bounded image regions using the respective one or more local features, the another respective one or more local features, and the respective one or more global features to provide an annotated content.