Journal
PATTERN RECOGNITION
Volume 52, Issue -, Pages 260-273Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.10.021
Keywords
Semantic segmentation; Random forest; DCT; Textons
Funding
- STMicroelectronics [65]
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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. (C) 2015 Elsevier Ltd. All rights reserved.
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