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Semantic segmentation of images exploiting DCT based features and random forest

Ravi, D, Bober, M, Farinella, GM, Guarnera, M and Battiato, S (2016) Semantic segmentation of images exploiting DCT based features and random forest Pattern Recognition, 52. pp. 260-273.

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Abstract

This paper presents an approach for generating class-specific image segmentation. We introduce two novel features that use the quantized data of the Discrete Cosine Transform (DCT) in a Semantic Texton Forest based framework (STF), by combining together colour and texture information for semantic segmentation purpose. The combination of multiple features in a segmentation system is not a straightforward process. The proposed system is designed to exploit complementary features in a computationally efficient manner. Our DCT based features describe complex textures represented in the frequency domain and not just simple textures obtained using differences between intensity of pixels as in the classic STF approach. Differently than existing methods (e.g., filter bank) just a limited amount of resources is required. The proposed method has been tested on two popular databases: CamVid and MSRC-v2. Comparison with respect to recent state-of-the-art methods shows improvement in terms of semantic segmentation accuracy.

Item Type: Article
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Ravi, DUNSPECIFIEDUNSPECIFIED
Bober, MUNSPECIFIEDUNSPECIFIED
Farinella, GMUNSPECIFIEDUNSPECIFIED
Guarnera, MUNSPECIFIEDUNSPECIFIED
Battiato, SUNSPECIFIEDUNSPECIFIED
Date : April 2016
Identification Number : https://doi.org/10.1016/j.patcog.2015.10.021
Copyright Disclaimer : © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Semantic segmentation, Random forest, DCT, Textons
Depositing User : Symplectic Elements
Date Deposited : 12 Sep 2016 08:49
Last Modified : 12 Sep 2016 08:49
URI: http://epubs.surrey.ac.uk/id/eprint/812097

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