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We compute effective distances between a set of body part pairs for a given image and feed to DNN to learn effective representation of complex actions. We also propose Distance-based Convolutional Neural Network (DCNN) to learn representations from 2D images. The distances are rearranged in 2D grayscale image called as a Distance Image. This 2D representation allows the network to learn specific discriminative information between adjacent pixel distance values corresponding to different body part pairs. We evaluate our method on two real-world datasets i.e. UT-Interaction and SBU Kinect Interaction. 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