Semantic segmentation of images exploiting DCT based features and random forest. (April 2016) - Ravì, D.; Bober, M.; Farinella, G.M.; Guarnera, M.; Battiato, S.
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. Abstract : Highlights: A method for semantic image segmentation based on random forests. Novel texture features based on Discrete Cosine Transform to describe image regions. The method uses a limited amount of resources and works in realtime. The approach shows good performance overcoming other state of the art. The system obtains a better accuracy on small classes (i.e., Pedestrians).