Scene parsing, or segmenting all the objects in an image and identifying their categories,
is one of the core problems of computer vision. In order to achieve an object-level
semantic segmentation, we build upon the recent superparsing approach by Tighe and
Lazebnik, which is a nonparametric solution to the image labeling problem.
Superparsing consists of four steps. For a new query image, the most similar images
from the training dataset of labeled images is retrieved based on global features. In
the second step, the query image is segmented into superpxiels and 20 di erent local
features are computed for each superpixel. We propose to use the SLICO segmentation
method to allow control of the size, shape and compactness of the superpixels
because SLICO is able to produce accurate boundaries. After all superpixel features
have been extracted, feature-based matching of superpixels is performed to nd the
nearest-neighbour superpixels in the retrieval set for each query superpxiel. Based on
the neighbouring superpixels a likelihood score for each class is calculated. Finally, we
formulate a Conditional Random Field (CRF) using the likelihoods and a pairwise cost
both computed from nonparametric estimation to optimize the labeling of the image.
Speci cally, we de ne a novel pairwise cost to provide stronger semantic contextual
constraints by incorporating the similarity of adjacent superpixels depending on local
features. The optimized labeling obtained with the CRF results in superpixels with the
same labels grouped together to generate segmentation results which also identify the
categories of objects in an image.
We evaluate our improvements to the superparsing approach using segmentation
evaluation measures as well as the per-pixel rate and average per-class rate in a labeling
evaluation. We demonstrate the success of our modi ed approach on the SIFT Flow
dataset, and compare our results with the basic superparsing methods proposed by
Tighe and Lazebnik.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/32329 |
| Date | January 2015 |
| Creators | Pan, Hong |
| Contributors | Jochen, Lang |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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