Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2015
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/1504.06507 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913002277568512 |
|---|---|
| author | Baltaxe, Michael Meer, Peter Lindenbaum, Michael |
| author_facet | Baltaxe, Michael Meer, Peter Lindenbaum, Michael |
| contents | The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object. One of the simplest and yet most effective oversegmentation algorithms is known as local variation (LV) (Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and show that algorithms similar to LV can be devised by applying different statistical models and decisions, thus providing further theoretical justification and a well-founded explanation for the unexpected high performance of the LV approach. Some of these algorithms are based on statistics of natural images and on a hypothesis testing decision; we denote these algorithms probabilistic local variation (pLV). The best pLV algorithm, which relies on censored estimation, presents state-of-the-art results while keeping the same computational complexity of the LV algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1504_06507 |
| institution | arXiv |
| publishDate | 2015 |
| record_format | arxiv |
| spellingShingle | Local Variation as a Statistical Hypothesis Test Baltaxe, Michael Meer, Peter Lindenbaum, Michael Computer Vision and Pattern Recognition The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object. One of the simplest and yet most effective oversegmentation algorithms is known as local variation (LV) (Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and show that algorithms similar to LV can be devised by applying different statistical models and decisions, thus providing further theoretical justification and a well-founded explanation for the unexpected high performance of the LV approach. Some of these algorithms are based on statistics of natural images and on a hypothesis testing decision; we denote these algorithms probabilistic local variation (pLV). The best pLV algorithm, which relies on censored estimation, presents state-of-the-art results while keeping the same computational complexity of the LV algorithm. |
| title | Local Variation as a Statistical Hypothesis Test |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1504.06507 |