By Topic

Toward a Sequential Approach to Pipelined Image Recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Rose, D. ; Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN, USA ; Arel, I.

This paper introduces a sequentially motivated approach to processing streams of images from datasets with low memory demands. We utilize fuzzy clustering as an incremental dictionary learning scheme and explain how the corresponding membership functions can be subsequently used in encoding features for image patches. We focus on replicating the codebook learning and classification stages from an established visual learning pipeline that has recently shown efficacy on the CIFAR-10 small image dataset. Experiments show that performance near batch oriented learning is achievable by combining naturally online learning mechanisms driven largely by stochastic gradient descent with strictly patch-wise operations. We further detail how back propagation can be used with a neural network classifier to modify parameters within the pipeline.

Published in:

Machine Learning and Applications (ICMLA), 2012 11th International Conference on  (Volume:2 )

Date of Conference:

12-15 Dec. 2012