Image clustering method and apparatus, electronic device, and storage medium

An image clustering apparatus, comprising:

• a processor;
• and
• a memory configured to store processor-executable instructions, wherein
• when the processor-executable instructions are executed by the processor, the processor is configured to:
• obtain a face feature and an attribute feature of each image in an image set;
• perform clustering processing on the image set by using a similarity of the face feature of each image in the image set to obtain at least one first class group, images comprised in each first class group being predicted images comprising a same object;
• determine a similarity matrix for each first class group by using the face features and the attribute features of the images in each first class group, each element in the similarity matrix representing a respective one of similarities between images in a first class group corresponding to the similarity matrix;
• and
• re-cluster the images in each first class group based on the similarity matrix corresponding to each first class group and the face features of the images in each first class group to obtain at least one second class group, images in the second class group being optimized predicted images having a same object.

A non-transitory computer-readable storage medium, having computer program instructions stored thereon, wherein when the computer program instructions are executed by a processor, the following operations are performed:

• obtaining a face feature and an attribute feature of each image in an image set;
• performing clustering processing on the image set by using a similarity of the face feature of each image in the image set to obtain at least one first class group, images comprised in each first class group being predicted images comprising a same object;
• determining a similarity matrix for each first class group by using the face features and the attribute features of the images in each first class group, each element in the similarity matrix representing a respective one of similarities between images in a first class group corresponding to the similarity matrix;
• and
• re-clustering the images in each first class group based on the similarity matrix corresponding to each first class group and the face features of the images in each first class group to obtain at least one second class group, images in the second class group being optimized predicted images having a same object.