A complex network approach for dynamic texture recognition

In this paper, we propose a novel approach for dynamic texture representation based on complex networks. In the proposed approach, each pixel of the video is mapped into a node of the complex network. Initially, a regular complex network is obtained by connecting two nodes if the Euclidean distance between their related pixels is equal or less than a given radius. For each connection, a weight is defined by the difference of the pixel intensities. Given the regular complex network, a function is applied to remove connections whose weight is equal to or below a given threshold. Finally, a feature vector is obtained by calculating the spatial and temporal average degree for networks transformed by different values of threshold and radius. The number of connections of pixels from the same frame and from different frames, respectively, gives the spatial and temporal degrees. Experimental results using synthetic and real dynamic textures have demonstrated the effectiveness of the proposed approach.