Layered Object Models for Image Segmentation
We formulate a layered model for object detection and image segmentation. We
describe a generative probabilistic model that composites the output of a bank
of object detectors in order to define shape masks and explain the appearance,
depth ordering, and labels of all pixels in an image. Notably, our system
estimates both class labels and object instance labels. Building on previous
benchmark criteria for object detection and image segmentation, we define a
novel score that evaluates both class and instance segmentation. We evaluate
our system on the PASCAL 2009 and 2010 segmentation challenge datasets and show
good test results with state of the art performance in several categories
including segmenting humans.
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Text Reference
Yi Yang, Sam Hallman, Deva Ramanan, and Charless Fowlkes. Layered object models for image segmentation. IEEE TPAMI, 2011.BibTeX Reference
@ARTICLE{YangHRF_TPAMI_2011,author = "Yang, Yi and Hallman, Sam and Ramanan, Deva and Fowlkes, Charless",
title = "Layered Object Models for Image Segmentation",
journal = "IEEE TPAMI",
year = "2011",
tag = "grouping"
}