Presentation Type

Poster

Start Date

5-10-2017 11:00 AM

End Date

5-10-2017 1:00 PM

Subjects

Image processing -- Digital techniques, Stochastic processes -- Mathematical models, Poisson processes

Abstract

We present a model that utilizes Cox processes and CNN classifiers in order to count the number of instances of an object in an image. Poisson processes are well suited to events that occur randomly in space, like the location of objects in an image, as well as to the task of counting. Mixed Poisson processes also offer increased flexibility, however they do not easily scale with image size: they typically require O(n3) computation time and O(n2) storage, where n is the number of pixels. To mitigate this problem, we employ Kronecker algebra which takes advantage of the direct product structure of the covariance matrix. As the likelihood is non Gaussian, we use Laplace Approximation for inference, which involves using the conjugate gradient and Newton’s method. Our approach has close to linear performance, requiring only O(n3/2) computation time and O(n) memory. In practice, we select a subset of bounding boxes in the image and we query them for the presence of the object by running a pre-trained CNN classifier like AlexNet. We aggregate the observations and compute a posterior distribution, which is then used to estimate the number of instances of the object in the entire image. We show results on both simulated data and on images from MS COCO dataset. We also compare our counting results with Faster RCNN, and show that for the task of counting, we out-perform or match the RCNN.

Rights

© Copyright the author(s)

IN COPYRIGHT:
http://rightsstatements.org/vocab/InC/1.0/
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DISCLAIMER:
The purpose of this statement is to help the public understand how this Item may be used. When there is a (non-standard) License or contract that governs re-use of the associated Item, this statement only summarizes the effects of some of its terms. It is not a License, and should not be used to license your Work. To license your own Work, use a License offered at https://creativecommons.org/

Persistent Identifier

http://archives.pdx.edu/ds/psu/20022

Share

COinS
 
May 10th, 11:00 AM May 10th, 1:00 PM

Cox Processes for Visual Object Counting

We present a model that utilizes Cox processes and CNN classifiers in order to count the number of instances of an object in an image. Poisson processes are well suited to events that occur randomly in space, like the location of objects in an image, as well as to the task of counting. Mixed Poisson processes also offer increased flexibility, however they do not easily scale with image size: they typically require O(n3) computation time and O(n2) storage, where n is the number of pixels. To mitigate this problem, we employ Kronecker algebra which takes advantage of the direct product structure of the covariance matrix. As the likelihood is non Gaussian, we use Laplace Approximation for inference, which involves using the conjugate gradient and Newton’s method. Our approach has close to linear performance, requiring only O(n3/2) computation time and O(n) memory. In practice, we select a subset of bounding boxes in the image and we query them for the presence of the object by running a pre-trained CNN classifier like AlexNet. We aggregate the observations and compute a posterior distribution, which is then used to estimate the number of instances of the object in the entire image. We show results on both simulated data and on images from MS COCO dataset. We also compare our counting results with Faster RCNN, and show that for the task of counting, we out-perform or match the RCNN.