Object cosegmentation using deep Siamese network
This work addresses object cosegmentation for computer vision applications, presenting an incremental improvement by integrating existing techniques like Siamese networks and ANNOY for retrieval.
The paper tackles the problem of segmenting similar objects from multiple images simultaneously, achieving results through an end-to-end pipeline that uses a deep Siamese network and object proposals, with evaluations conducted using numerical metrics.
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. Finally, we form a collage from the segmented similar objects based on the relative importance of the objects.