Random Geometric Prior Forest for Multiclass
Object Segmentation
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com
Abstract
Recent advances in object detection have led to the development of segmentation by detection
approaches that integrate top-down geometric priors for multiclass object segmentation. A key yet
under-addressed issue in utilizing top-down cues for the problem of multiclass object segmentation
by detection is efficiently generating robust and accurate geometric priors. In this paper, we
propose a random geometric prior forest scheme to obtain object-adaptive geometric priors
efficiently and robustly. In the scheme, a testing object first searches for training neighbors with
similar geometries using the random geometric prior forest, and then the geometry of the testing
object is reconstructed by linearly combining the geometries of its neighbors. Our scheme enjoys
several favorable properties when compared with conventional methods. First, it is robust and
very fast because its inference does not suffer from bad initializations, poor local minimums or
complex optimization. Second, the figure/ground geometries of training samples are utilized in a
multitask manner. Third, our scheme is object-adaptive but does not require the labeling of parts
or poselets, and thus, it is quite easy to implement. To demonstrate the effectiveness of the
proposed scheme, we integrate the obtained top-down geometric priors with conventional bottomup color cues in the frame of graph cut. The proposed random geometric prior forest achieves the
best segmentation results of all of the methods tested on VOC2010/2012 and is 90 times faster
than the current state-of-the-art method.
Existing Method:
A feasible solution for this problem is to derive the geometric prior from a mixture model of
deformable parts that includes both coarse “root” and fine “part” detections.
Demerits
High complexity
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com
Proposed Method
we propose a random geometric prior forest scheme to obtain object-adaptive geometric priors
efficiently and robustly.
Merits:
Execution time is less
Better performance
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com