Object cosegmentation using deep Siamese network

Understand

Object cosegmentation addresses the problem of discovering similar objects from multiple images and segmenting them as foreground simultaneously.

• In this paper, we propose a novel end-to-end pipeline to segment the similar objects simultaneously from relevant set of images using supervised learning via deep-learning framework.
• We experiment with multiple set of object proposal generation techniques and perform extensive numerical evaluations by training the Siamese network with generated object proposals.
• Similar objects proposals for the test images are retrieved using the ANNOY (Approximate Nearest Neighbor) library and deep semantic segmentation is performed on them.