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In this work, we present a random forest framework that learns the weights,shapes, and sparsities of feature representations for real-time semanticsegmentation. Typical filters (kernels) have predetermined shapes andsparsities and learn only weights. A few feature extraction methods fix weightsand learn only shapes and sparsities. These predetermined constraints restrictlearning and extracting optimal features. To overcome this limitation, wepropose an unconstrained representation that is able to extract optimalfeatures by learning weights, shapes, and sparsities. We, then, present therandom forest framework that learns the flexible filters using an iterativeoptimization algorithm and segments input images using the learnedrepresentations. We demonstrate the effectiveness of the proposed method usinga hand segmentation dataset for hand-object interaction and using two semanticsegmentation datasets. The results show that the proposed method achievesreal-time semantic segmentation using limited computational and memoryresources.

Random Forest with Learned Representations for Semantic Segmentation