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A neural ensemble approach for segmentation and classification of road images

conference contribution
posted on 06.12.2017, 00:00 authored by Tejy Kinattukara JobachanTejy Kinattukara Jobachan, Brijesh VermaBrijesh Verma
This paper presents a novel neural ensemble approach for classification of roadside images and compares its performance with three recently published approaches. In the proposed approach, an ensemble neural network is created by using a layered k-means clustering and fusion by majority voting. This approach is designed to improve the classification accuracy of roadside images into different objects like road, sky and signs. A set of images obtained from Transport and Main Roads Queensland is used to evaluate the proposed approach. The results obtained from experiments using proposed approach indicate that the new approach is better than the existing approaches for segmentation and classification of roadside images.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

183

End Page

193

Number of Pages

11

Start Date

01/01/2014

Finish Date

01/01/2014

ISBN-13

9783319126425

Location

Kuching, Sarawak, Malaysia

Publisher

Springer

Place of Publication

Switzerland

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

ICONIP (Conference)