This research investigates the features retained after image compression for automatic pattern recognition purposes. Many raw images with vehicles in them were collected for these experiments. These raw images were significantly compressed using open-source JPEG and JPEG2000 compression algorithms. The original and compressed images are processed with a Map Seeking Circuit (MSC) pattern recognition algorithm, as well as a Histogram of Oriented Gradient (HOG) with Support Vector Machine (SVM) pattern recognition program. Detection rates are given for these images that demonstrates the feature extraction capabilities as well as false alarm rates when the compression was increased. JPEG2000 compression results show preservation of the features needed for automatic pattern recognition which was better than the JPEG standard image compression results.

Date Published: 1 May 2017
PDF: 10 pages
Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 102030F (1 May 2017); doi: 10.1117/12.2262919

Published in SPIE Proceedings Vol. 10203:
Pattern Recognition and Tracking XXVIII
Mohammad S. Alam, Editor(s)