Texture is the collective repetitive pattern that characterizes the surface of real world objects. The main challenge in the texture description is its application specific definition. The present work aims at bringing the definition of textures under a generalized framework and propose some texture descriptors. In order to accomplish this, authors have extensively studied the properties of texture, drawn four observations and used some of them to devise two texture descriptors under the framework of multi-scale mathematical morphology and co-occurrence matrices. Thereafter, the descriptors are used for texture classification and tested on three benchmark datasets. Before applying the descriptors to texture classification, a dependence between number of decomposition levels (scales) and classification percentage is established using hypothesis testing. Once the dependence is established, the corresponding scale and distance parameter is chosen for each dataset. The classification results are compared with a number of existing methods. The efficacy of results prove the supremacy of the proposed methods over the existing ones.