Semantic segmentation of images exploiting DCT based features and random forest

D. Ravì, M. Bober, G. M. Farinella, M. Guarnera, S. Battiato

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)260-273
Number of pages14
JournalPattern Recognition
Publication statusPublished - 1 Apr 2016


  • DCT
  • Random forest
  • Semantic segmentation
  • Textons


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