Sign Classification using Local and Meta-Features
Abstract
Our world is populated with visual information that a sighted person makes use of daily. Unfortunately, the visually impaired are deprived of such information, which limits their mobility in unconstrained environments. To help alleviate this we are developing a wearable system that is capable of detecting and recognizing signs in natural scenes. The system is composed of two main components, sign detection and recognition. The sign detector, uses a conditional maximum entropy model to find regions in an image that correspond to a sign. The sign recognizer matches the hypothesized sign regions with sign images in a database. It then uses the match scores to compute meta-features and train a classifier to decide if the most likely sign is correct or if the hypothesized sign region does not belong to a sign in the database. Our data sets encompass a wide range of variability including changes in lighting, orientation and viewing angle. In this paper, we present an overview of the system while while paying particular attention to the recognition component. Tested on 3,975 sign images from two different data sets, the recognition phase achieves accuracies of 99.5% with 35 distinct signs and 92.8% with 65 distinct signs.


BiBTex Entry
@inproceedings{mattar-sign-cvavi05,
author = {Marwan A. Mattar and Allen R. Hanson and Erik G. Learned-Miller},
title = {Sign Classification using Local and Meta-Features},
booktitle = {Proceedings of the IEEE Workshop on Computer Vision Applications for the Visually Impaired},
location = {San Diego, CA},
month = {June},
year = {2005}
}