A novel scheme based on local binary pattern for dynamic texture recognition

Dynamic textures (DTs) are moving sequences of natural scenes with some form of temporal regularity such as boiling water, a flag fluttering in the wind. The motion causes continuous changes in the geometry of dynamic textures thus it is difficult to apply traditional vision algorithms to recognize this class of textures. This paper proposes a scheme for modeling and classification of dynamic textures using a local image descriptor which efficiently encodes texture information in a space-time domain. The proposed descriptor extends the well-known spatial texture descriptor, local binary pattern (LBP), to spatio-temporal domain in order to represent the DT by combining appearance feature with the motion. Although, local binary patterns are used extensively in visual recognition applications due to their excellent performance and computational simplicity, but sometimes unable to differentiate different structures properly due to their dependency on center pixel as a threshold. In this paper, a new descriptor based on a global adaptive threshold is employed to compute the structure pattern of local image patch which differentiates various local image structures more efficiently. However, the LBP pattern defines the spatial structure of a local image patch but it does not give information about the contrast of local image patch. We have used Michelson contrast to compute the difference in luminance in the local texture and clubbed with local structure pattern computed using the proposed descriptor. Extensive experiments on dynamic texture databases (Dyntex, Dyntex++ and UCLA) prove the efficiency of the proposed method.