Semantic Image Labeling with Histograms of Oriented Gradient and Gray Level Co-occurrence Matrix

DOI

10.2991/icwcsn-16.2017.107How to use a DOI?

Keywords

semantic image labeling; Histograms of Oriented Gradients; Gray Level Co-occurrence Matrix; accuracy; Approximate Nearest Neighbors.

Abstract

In this paper, we propose a new approach for semantic image labeling by incorporating texture, gradient and color information. In our paper, the texture information is extracted by Gray Level Co-occurrence Matrix (GLCM). The gradient information is obtained by Histograms of Oriented Gradients (HOG). We apply the HOG, GLCM descriptors with color information simultaneously to enrich the image features of different information. To utilizing these features more effectively, we use the Approximate Nearest Neighbors (ANN) algorithm for clustering. After obtaining these information, the Joint Boost algorithm is applied to give an effective classifier by training many weak learner classifiers. At the end, a set of experiments with one descriptor or several descriptors combined are made to evaluating the performance of our method.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jian-She Ma
AU  - Tong Liu
AU  - Xiu-Tian Huang
AU  - Ping Su
PY  - 2016/12
DA  - 2016/12
TI  - Semantic Image Labeling with Histograms of Oriented Gradient and Gray Level Co-occurrence Matrix
BT  - Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
PB  - Atlantis Press
SP  - 527
EP  - 531
SN  - 2352-538X
UR  - https://doi.org/10.2991/icwcsn-16.2017.107
DO  - 10.2991/icwcsn-16.2017.107
ID  - Ma2016/12
ER  -