Name
Abstract | ||
|---|---|---|
We propose a computationally efficient graph based image co- segmentation algorithm where we extract objects with similar features from an image pair or a set of images. First we build a region adjacency graph (RAG) for each image by representing image superpixels as nodes. Then we compute the maximum common subgraph (MCS) between the RAGs using the minimum vertex cover of a product graph obtained from the RAG. Next using MCS outputs as the seeds, we iteratively co- grow the matched regions obtained from the MCS in each of the constituent images by using a weighted measure of inter-image feature similarities among the already matched regions and their neighbors that have not been matched yet. Upon convergence, we obtain the co- segmented objects. The MCS based algorithm allows multiple, similar objects to be co- segmented and the region co- growing stage helps to extract different sized, similar objects. Superiority of the proposed method is demonstrated by processing images containing different sized objects and multiple objects. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46466-4_44 | COMPUTER VISION - ECCV 2016, PT VI |
Keywords | Field | DocType |
Maximum common subgraph, Region co-growing | Convergence (routing),Adjacency list,Graph,Pattern recognition,Segmentation,Computer science,Artificial intelligence,Vertex cover,Factor-critical graph | Conference |
Volume | ISSN | Citations |
9910 | 0302-9743 | 3 |
PageRank | References | Authors |
0.37 | 25 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Avik Hati | 1 | 9 | 1.81 |
| Subhasis Chaudhuri | 2 | 1384 | 133.18 |
| Rajbabu Velmurugan | 3 | 61 | 11.64 |