Automatically generating an image dataset based on object instance similarity

A system comprising:

• at least one memory device comprising digital images having metadata;
• and
• at least one server device configured to cause the system to:
• select a series of correlated images from the digital images utilizing the metadata associated with the digital images by grouping digital images having related metadata tags;
• extract objects portrayed in the series of correlated images by generating background masks for the series of correlated images utilizing an object detection model and a segmentation model;
• generate content embeddings and color embeddings for the extracted objects by utilizing a convolutional neural network trained to generate semantic image labels and a color embedding model;
• generate object clusters by grouping semantically similar objects from the content embeddings and the color embeddings utilizing a clustering algorithm by mapping combined embeddings in a query space;
• and
• deduplicate the object clusters by merging nearest object clusters of the object clusters based on a threshold distance value;
• and
• build a digital image similarity machine learning model by:
• selecting a pair of ground truth similar images from an object cluster of the deduplicated object clusters;
• and
• utilizing the pair of ground truth similar images to train the digital image similarity machine learning model.

In a digital medium environment for processing a repository of digital images, a computer-implemented method comprising:

• select a series of correlated images from digital images utilizing metadata associated with the digital images;
• extracting objects portrayed in a series of correlated images by:
• detecting the objects in the series of correlated images by utilizing an object detection model;
• generating segmentation masks of the objects in the series of correlated images by utilizing a segmentation model;
• and
• removing image data not associated with the segmentation masks from the series of correlated images to generate extracted objects;
• generating content embeddings and color embeddings for the extracted objects;
• generating object clusters by grouping the content embeddings and the color embeddings utilizing a clustering algorithm;
• utilizing one or more of the object clusters and corresponding digital images to build a digital image similarity machine learning model;
• deduplicating the object clusters by merging nearest object clusters of the object clusters based on a threshold distance value;
• and
• utilizing one or more of the deduplicated object clusters to build the digital image similarity machine learning model by:
• selecting a pair of ground truth similar images from an object cluster of the deduplicated object clusters;
• and
• utilizing the pair of ground truth images to train the digital image similarity machine learning model.